openNCA computation engine is an R package that provides for generation of pharmacokinetic parameter estimates using non-compartmental (NCA) pharmacokinetic analysis methods.
License: MIT License
in run_computation.R: the following code needs updating
```
if("MRTEVIFPi" %in% parameter_list && (!"AUCINFPi" %in% parameter_list || !"AUCTAUi" %in% parameter_list || !"AUMCTAUi" %in% parameter_list)){
if(!"AUCINFPi" %in% parameter_list){
parameter_list[[length(parameter_list)+1]] <- "AUINFPi"
}
to
if("MRTEVIFPi" %in% parameter_list && (!"AUCINFPi" %in% parameter_list || !"AUCTAUi" %in% parameter_list || !"AUMCTAUi" %in% parameter_list)){
if(!"AUCINFPi" %in% parameter_list){
parameter_list[[length(parameter_list)+1]] <- "AUCINFPi"
}
2)
if("MRTEVIFOi" %in% parameter_list && (!"AUCINFOi" %in% parameter_list || !"AUCTAUi" %in% parameter_list || !"AUMCTAUi" %in% parameter_list)){
if(!"AUCINFOi" %in% parameter_list){
parameter_list[[length(parameter_list)+1]] <- "AUINFOi"
}
to
```
if("MRTEVIFOi" %in% parameter_list && (!"AUCINFOi" %in% parameter_list || !"AUCTAUi" %in% parameter_list || !"AUMCTAUi" %in% parameter_list)){
if(!"AUCINFOi" %in% parameter_list){
parameter_list[[length(parameter_list)+1]] <- "**AUCINFOi**"
}
running tc014 with CTROUGHi specified through PARAMETERLIST causes crash:
> results_list <- run_computation(data=d, map=mct, flag=flags, parameterset=parameterset)
Error in value[[3L]](cond) :
Error in ctrough(conc = tmp_di_df[, map_data$CONC], time = tmp_di_df[, : object 'tmp_di_df' not found
For SDEID 50896291
In addition: Warning message:
In run_M2_SS_computation(data = merged_data, map = map_data, method = method, :
Show Traceback
Rerun with Debug
Error in value[[3L]](cond) :
Error in ctrough(conc = tmp_di_df[, map_data$CONC], time = tmp_di_df[, : object 'tmp_di_df' not found
For SDEID 50896291
There's nothing obviously distinct about SDEID==50896291
SDEID SUBJID PKDATAROWID STUDY SITEID RAND TREATXT TRTCD PKCOLL PKBDFLD PKTERM PERIODU PERIOD VISITU VISIT PHASE PKACES PKSCOM PKUSMID PKSAMQA NTPDU PKAACES
108 50896291 10021020 28860392 A4021010 1002 2020 NA 9999 POINT Plasma CP-751,871 Cycle 4 Day 1 NA PK30-01863171 0 690 FINAL Hour 687
109 50896291 10021020 28860393 A4021010 1002 2020 NA 9999 POINT Plasma CP-751,871 Cycle 4 Day 1 NA PK1-01863181 0 691 FINAL Hour 688
110 50896291 10021020 -28860394 A4021010 1002 2020 NA 9999 POINT Plasma CP-751,871 Cycle 4 Day 1 NA PK24-01863112 0 692 FINAL Hour 689
111 50896291 10021020 -28860396 A4021010 1002 2020 NA 9999 POINT Plasma CP-751,871 Cycle 4 Day 1 NA PK-01863136 0 694 FINAL Hour 690
112 50896291 10021020 -28860400 A4021010 1002 2020 NA 9999 POINT Plasma CP-751,871 Cycle 4 Day 1 NA PK-01863135 0 698 FINAL Hour 694
113 50896291 10021020 -28860411 A4021010 1002 2020 NA 9999 POINT Plasma CP-751,871 Cycle 4 Day 1 NA PK-01863137 0 709 FINAL Hour 705
114 50896291 10021020 -28860414 A4021010 1002 2020 NA 9999 POINT Plasma CP-751,871 Cycle 4 Day 1 NA PK30-01863192 0 712 FINAL Hour 708
PKACOM PKAMT PKAMTU PKCNC PKCNCU PKCOML PKPTMR NTPDMN PKCNCN PKPTM PKPTMI PKPTMS PKPTME PKPTMU PKCOMC PKATM PKADT PKSMND PKND PKSMMSU PKSMMS PKSMVLU PKSMVL PKATME PKADTE
108 NA 0 NA 198000 ng/mL NA 0 NA 198000 0HR NA 0 NA HR NA 1026 01 11 2007 DONE DONE NA NA NA NA NA NA
109 NA 0 NA 843000 ng/mL NA 1 NA 843000 1HR NA 1 NA HR NA 1653 01 11 2007 DONE DONE NA NA NA NA NA NA
110 NA 0 NA 536000 ng/mL NA 24 NA 536000 24HR NA 24 NA HR NA 1556 02 11 2007 DONE DONE NA NA NA NA NA NA
111 NA 0 NA 481000 ng/mL NA NA NA 481000 NA NA NA HR NA 1236 04 11 2007 DONE DONE NA NA NA NA NA NA
112 NA 0 NA 333000 ng/mL NA NA NA 333000 NA NA NA HR NA 1140 08 11 2007 DONE DONE NA NA NA NA NA NA
113 NA 0 NA 208000 ng/mL NA NA NA 208000 NA NA NA HR NA 950 22 11 2007 DONE DONE NA NA NA NA NA NA
114 NA 0 NA 225000 ng/mL NA 0 NA 225000 0HR NA 672 NA HR NA 1021 29 11 2007 DONE DONE NA NA NA NA NA NA
DOSE DOSEUNI COHORT DOSE.FREQ INF.START PTINIT HT WT AGEDERU AGEDER WTUNI WTRAW HTUNI HTRAW RACEOTH RACES SEX DOB RACIALD ETHNIC DRGDATE DOSETIM
108 1106 MG 20 MG/KG RP2D ES 4 11:11 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 01 11 2007 1558
109 1106 MG 20 MG/KG RP2D ES 4 11:11 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 01 11 2007 1558
110 1106 MG 20 MG/KG RP2D ES 4 11:11 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 01 11 2007 1558
111 1106 MG 20 MG/KG RP2D ES 4 11:11 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 01 11 2007 1558
112 1106 MG 20 MG/KG RP2D ES 4 11:11 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 01 11 2007 1558
113 1106 MG 20 MG/KG RP2D ES 4 11:11 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 01 11 2007 1558
114 1102 MG 20 MG/KG RP2D ES 4 10:46 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 01 11 2007 1558
ACTTRT ACTTRTS ACTTRTC PKATPD PKATPDU PKATPDE PKATPDEU PKDCOM LCDF C0 CONC.BC CONC.BCU PKCNC.BC PKTERM.BC ACONC
108 CP-751,871 dose escalation A NA 0.000 HR NA HR Y 0 198 MG/L 198 CP-751,871_Corrected 198
109 CP-751,871 dose escalation A NA 0.917 HR NA HR Y 0 843 MG/L 843 CP-751,871_Corrected 843
110 CP-751,871 dose escalation A NA 23.967 HR NA HR Map Day 2 to Day 1 Y 0 536 MG/L 536 CP-751,871_Corrected 536
111 CP-751,871 dose escalation A NA NA HR NA HR Map Day 4 to Day 1 Y 0 481 MG/L 481 CP-751,871_Corrected 481
112 CP-751,871 dose escalation A NA NA HR NA HR Map Day 8 to Day 1 Y 0 333 MG/L 333 CP-751,871_Corrected 333
113 CP-751,871 dose escalation A NA NA HR NA HR Map Day 15 to Day 1 Y 0 208 MG/L 208 CP-751,871_Corrected 208
114 CP-751,871 dose escalation A NA 666.383 HR NA HR Map Cycle 5 predose to Cycle 4 672 hr Y 0 225 MG/L 225 CP-751,871_Corrected 225
ACONC.CHAR NTIME ATIME TAU TOLD DOSE1
108 198 0 0.000 672 0 1106
109 843 1 0.917 672 0 1106
110 536 24 23.967 672 0 1106
111 481 72 68.633 672 0 1106
112 333 168 163.700 672 0 1106
113 208 336 497.867 672 0 1106
114 225 672 666.383 672 0 1106
Test case tc017 M4SD was executed with mct$FLGACCEPTKELCRIT set to
"KELRSQ>=0.9,KELNOPT>=3,AUCXPCTP<=20.0"
However, AUCXPCTP is not computed by the M4SD model.
> results_list <- run_computation(data=d, map=mct, flag=flags, parameterset=parameterset)
Error in `[.data.frame`(computation_df, , flag_df$VAR[f]) :
undefined columns selected
In addition: Warning messages:
1: In run_M4_SD_computation(data = merged_data, map = map_data, method = method, :
Flag 'FLGACCEPTKELCRIT' values provided via 'map' does not have a valid parameter name 'AUCXPCTP'
2: In run_M4_SD_computation(data = merged_data, map = map_data, method = method, :
Show Traceback
Rerun with Debug
Error in `[.data.frame`(computation_df, , flag_df$VAR[f]) :
undefined columns selected
When FLGACCEPTKELCRIT is reset to "KELRSQ>=0.9,KELNOPT>=3" and run_computation re-executed, the computation runs as expected.
tc012 M1SS amplified to include a 3rd dosing interval with TAU/TOLD extending beyond extent of the data. Expected behavior is for the parameters that can be reasonably computed (AUCTAU for example) should be computed with value of the concentration data point extrapolated based upon the estimated KEL/slope. The observed behavior is that the computation engine crashes unexpectedly.
> testcase.id <- "tc012"
> p <- "./testcases/tc012"
> params["INPUT1"] = file.path(p, "A0081216.in.csv")
> params["parameterset"] ="PARAMETERLIST"
> params["map"] = file.path(p, "tc012_mct_updated.csv")
> params["flags"] = file.path(p, "A0081216_flag_data.in.csv")
> params["OUTPUT"] ="."
> print(params)
INPUT1 parameterset map
"./testcases/tc012/A0081216.in.csv" "PARAMETERLIST" "./testcases/tc012/tc012_mct_updated.csv"
flags OUTPUT
"./testcases/tc012/A0081216_flag_data.in.csv" "."
>
> mct <- read_csv(params["map"])
Parsed with column specification:
cols(
.default = col_character(),
AUCNPAIR = col_integer(),
FLGNOCMAX = col_integer(),
FLGACCEPTPREDOSECRIT = col_double(),
SPANRATIOCRIT = col_integer(),
DP.DEFAULT = col_integer(),
SF.DEFAULT = col_integer(),
OPTIMIZEKEL = col_integer(),
INCLUDEINTERPOLATION = col_integer(),
INCLUDEEXTRAPOLATION = col_integer(),
AUC.1.T1 = col_integer(),
AUC.1.T2 = col_integer(),
AUC.2.T1 = col_integer(),
AUC.2.T2 = col_integer(),
AUC.3.T1 = col_integer(),
AUC.3.T2 = col_integer(),
AUC.4.T1 = col_integer(),
AUC.4.T2 = col_integer(),
AUC.5.T1 = col_integer(),
AUC.5.T2 = col_integer()
)
See spec(...) for full column specifications.
> write_file(toJSON(mct, na="string"), path=file.path(p, "tc012_mct_updated.json"))
> params["map"] = file.path(p, "tc012_mct_updated.json")
>
> flags <- read_csv(params["flags"])
Parsed with column specification:
cols(
PKDATAROWID = col_integer(),
FLGEXSDE = col_integer(),
FLGEXKEL = col_integer(),
FLGEXAUC = col_integer(),
FLGEXST = col_integer(),
NDOSEI = col_integer(),
PKPCOM = col_character(),
MIDPNTN = col_character(),
MIDPNTA = col_character(),
MIDPNTNU = col_character(),
MIDPNTAU = col_character(),
RATEN = col_character(),
RATEA = col_character(),
RATENU = col_character(),
RATEAU = col_character()
)
> write_file(toJSON(flags, na="string"), path=file.path(p, "tc012_flags.json"))
> params["flags"] = file.path(p, "tc012_flags.json")
> conc <- read.csv(file=params["INPUT1"], header=TRUE, sep=",", as.is=TRUE)
> vlist <- c("PKDATAROWID", "PKPTMS", "PKCNCN", "PERIOD", "TREATXT")
>
> # Read mct
> mct <- params["map"]
> ###mct <- read_file(mct)
> ###mct <- gsub("[\\]+", "", mct)
> mct <- fromJSON(mct)
>
> # Read flags
> flags <- params["flags"]
> ###flags <- read_file(flags)
> ###flags <- gsub("[\\]+", "", flags)
> flags <- fromJSON(flags)
>
> # ParameterSet
> if(is.na(params["parameterset"])) { params["parameterset"] <- "PARAMETERDISPLAYLIST" }
> parameterset <- params["parameterset"]
>
> # FLAGS is a subset for the specific profile, subset concentration dataframe
> # to those records by PKDATAROWID
> d <- conc[is.element(conc$PKDATAROWID, flags$PKDATAROWID),]
>
> # Add in missing required parameters for virtual compute call
> klist <- c("KEL", "KELRSQ", "KELNOPT", "THALFF")
> if(!is.element("PARAMETERDISPLAYLIST", names(mct))) { mct$PARAMETERDISPLAYLIST <- "" }
> for(i in klist) {
+ ###if(regexpr(i, mct$PARAMETERDISPLAYLIST, ignore.case=TRUE)==-1) { mct$PARAMETERDISPLAYLIST <- c(mct$PARAMETERDISPLAYLIST, i) }
+ if(regexpr(i, mct$PARAMETERDISPLAYLIST, ignore.case=TRUE)==-1) { mct$PARAMETERDISPLAYLIST <- paste(mct$PARAMETERDISPLAYLIST, i, sep=";") }
+ }
>
> # Remove any leading semi-colons ";"
> mct$PARAMETERDISPLAYLIST <- gsub("^;*", "", mct$PARAMETERDISPLAYLIST, ignore.case=TRUE, perl=TRUE)
>
> # For virtual compute call only: Subset the parameters to be calculated to those in parameterdisplaylist
> if(!is.element("PARAMETERLIST", names(mct))) { mct$PARAMETERLIST <- "" }
>
> # Check whether TIME selection has been set in MCT - default to NOMINAL
> if(!is.element("TIME", names(mct))) { mct$TIME <- "NOMINAL" }
>
> # Determine whether there are any points selected for inclusion in the terminal elimination phase via
> # FLGEXKEL (0=not excluded/included; 1=excluded/not included)
> # ***What happens if there are no flags provided?***
> n <- factor(flags$FLGEXKEL, levels=c(0,1))
> ninc <- length(n[n==0])
> nexc <- length(n[n==1])
> model_spec(mct)
[1] "openNCA: Model: M1 DosingType: SS AUCMETHOD: LINLOG"
> mct$TIME
[1] "NOMINAL"
>
> mct$PARAMETERDISPLAYLIST
[1] "SUBJID;TREATXT;PKBDFLD;PKTERM;PERIODU;PERIOD;VISITU;VISIT;DOSE;DOSEU;ACTTRT;AUCTAU;AUCLASTi;AUCXPCTP;CLFTAUi;CMAX;CMAXi;KEL;KELNOPT;KELRSQ;KELTMHI;KELTMLO;THALF;TLAST;TMAX;TMAXi;VZFP;CAV;CTROUGH;CMIN;PTR;PTF;RACC;RSS;RACCMAXi;THALFF"
> mct$PARAMETERLIST
[1] "AUCALL;AUCALLDN;AUCINFO;AUCINFOC;AUCINFODN;AUCINFP;AUCINFPC;AUCINFPDN;AUCLAST;AUCLASTi;AUCLASTDNi;AUCLASTCi;AUCT;AUCT1_T2;AUCTAUi;AUCTAUDNi;AUCXPCTO;AUCXPCTP;AUMCTAUi;CAVi;CLASTi;CLFO;CLFTAUi;CLFTAUWi;CLO;CLRTAUi;CMAXi;CMAXCi;CMAXDNi;CMINi;DOSECi;CTROUGHi;CTROUGHENDi;DIi;DOSEi;KEL;KELNOPT;KELRSQ;KELRSQA;KELTMHI;KELTMLO;LASTTIME;MRTEVIFOi;MRTEVIFPi;PTFi;PTRi;PTROUGHRi;PTROUGHREND;TAU;TAUi;THALF;THALFF;TLAG;TLAST;TMAXi;TMINi;TOLD;VZFP;VZFTAUi;VZFTAUWi"
>
> parameterset <- "PARAMETERDISPLAYLIST"
> parameterset <- "PARAMETERLIST"
> parameterset
[1] "PARAMETERLIST"
>
> mct$CONCOUTPUTUNIT <- "UG/ML"
> unique(d$PKCNCU)
[1] "UG/ML"
>
> mct$DOSEOUTPUTUNIT
[1] "MG"
> mct$DOSE1U
[1] "DOSEUNI"
> unique(d[,mct$DOSE1U])
[1] "MG"
>
> # Parmeters for M1SS
> model_parameters("M1|M1SS")
[1] "AUCALL" "AUCALLDN" "AUCDN" "AUCINFO" "AUCINFOC" "AUCINFODN" "AUCINFOi" "AUCINFP" "AUCINFPC" "AUCINFPDN" "AUCINFPi"
[12] "AUCLAST" "AUCLASTi" "AUCLASTC" "AUCLASTCi" "AUCLASTDN" "AUCLASTDNi" "AUCT" "AUCT1_T2" "AUCTAUDNi" "AUCTAU" "AUCTAUi"
[23] "AUCTDN" "AUCXPCTO" "AUCXPCTOi" "AUCXPCTP" "AUCXPCTPi" "AUMCINFO" "AUMCINFOi" "AUMCINFP" "AUMCINFPi" "AUMCLAST" "AUMCLASTi"
[34] "AUMCTAUi" "AUMCXPTO" "AUMCXPTOi" "AUMCXPTP" "AUMCXPTPi" "CAVi" "CEST" "CLAST" "CLASTi" "CLFO" "CLFOW"
[45] "CLFP" "CLFPW" "CLFTAUi" "CLFTAUWi" "CMAX" "CMAXi" "CMAXDN" "CMAXDNi" "CMIN" "CMINi" "CMINDN"
[56] "CMINDNi" "CTROUGHi" "CTROUGHENDi" "DIi" "DOSE" "DOSEi" "DOSEC" "DOSECi" "F" "FREL" "FRELLAST"
[67] "FRELLASTi" "FTAUi" "KEL" "KELC0" "KELNOPT" "KELR" "KELRSQ" "KELRSQA" "KELTMHI" "KELTMLO" "LASTTIME"
[78] "LASTTIMEi" "MRTEVIFO" "MRTEVIFOi" "MRTEVIFP" "MRTEVIFPi" "MRTLAST" "MRTLASTi" "PTF" "PTFi" "PTR" "PTRi"
[89] "PTROUGHRi" "PTROUGHRENDi" "RACi" "RACCMAXi" "RACCMINi" "RSSi" "TAU" "TAUi" "THALF" "THALFF" "TLAG"
[100] "TLAST" "TLASTi" "TMAX" "TMAXi" "TMIN" "TMINi" "TOLD" "TOLDi" "VZFO" "VZFOW" "VZFP"
[111] "VZFPW" "VZFTAUi" "VZFTAUWi"
>
> k <- parameter_required("OUTPUTUNIT$", names(mct), simplify=FALSE)
> mct[,names(k)]
TIMEOUTPUTUNIT AMOUNTOUTPUTUNIT DOSEOUTPUTUNIT VOLUMEOUTPUTUNIT CONCOUTPUTUNIT KELOUTPUTUNIT CLOUTPUTUNIT AUCOUTPUTUNIT AUMCOUTPUTUNIT AUCNORMOUTPUTUNIT AUROUTPUTUNIT
1 HR NA MG L UG/ML 1/HR L/HR NG.HR/ML NG.HR.HR/ML NG.HR/ML/MG NA
CONCNORMOUTPUTUNIT RATEOUTPUTUNIT VOLUMENORMOUTPUTUNIT CLNORMOUTPUTUNIT
1 NG/ML/MG NA L/KG ML/HR/KG
>
> k <- parameter_required("^DOSE", names(mct), simplify=FALSE)
> mct[,names(k)]
DOSE1 DOSE1U DOSEOUTPUTUNIT DOSETIM DOSETIMESTOP DOSE2 DOSE2U
1 DOSE1 DOSEUNI MG DOSETIME DOSETIMESTOP DOSE2 DOSEUNI
>
> mct$DOSE3 <- "DOSE3"
> mct$DOSE3U <- "DOSEUNI"
>
> d$DOSE3 <- d$DOSE2
>
> k <- parameter_required("^DOSE", names(mct), simplify=FALSE)
> mct[,names(k)]
DOSE1 DOSE1U DOSEOUTPUTUNIT DOSETIM DOSETIMESTOP DOSE2 DOSE2U DOSE3 DOSE3U
1 DOSE1 DOSEUNI MG DOSETIME DOSETIMESTOP DOSE2 DOSEUNI DOSE3 DOSEUNI
>
> k <- parameter_required("^TAU", names(mct), simplify=FALSE)
> mct[,names(k)]
TAU1 TAU1U TAU2 TAU2U
1 TAU1 PKPTMU TAU2 PKPTMU
> k <- parameter_required("^TOLD", names(mct), simplify=FALSE)
> mct[,names(k)]
TOLD1 TOLD1U TOLD2 TOLD2U
1 TOLD1 TOLD1U TOLD2 PKPTMU
>
> mct$TOLD3 <- "TOLD3"
> mct$TAU3 <- "TAU3"
>
> d$TAU1
[1] 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 12 12 12 12 12 12 12 12 12 12 12 12 12 12
[60] 12 12 12 12 12 24 24 24 24 24 24 24 24 24 24 24 24 24 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 24 24 24
[119] 24 24 24 24 24 24 24 24 24 24 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 12 12 12 12
[178] 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 24 24 24 24 24 24 24 24 24 24 24 24 24 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
[237] 12 12 12 12 12 12 12 24 24 24 24 24 24 24 24 24 24 24 24 24 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24
[296] 24 24 24 24 24 24 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 24 24 24 24 24 24 24 24 24 24 24 24 24 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
[355] 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 24 24 24 24 24 24 24 24 24 24 24 24 24 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 24 24 24 24 24 24 24 24 24 24
[414] 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 24 24 24 24 24 24 24 24 24 24 24 24 24 12 12 12 12 12 12 12 12 12 12 12
[473] 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 24 12 12 12 12 12 12
[532] 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 24 24 24 24 24 24 24 24 24 24 24 24 24
> d$TOLD1
[1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[90] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[179] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[268] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[357] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[446] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[535] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
> d$TAU2
[1] 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 12 12 12 12 12 12 12 12 12 12 12 12 12 12
[60] 12 12 12 12 12 NA NA NA NA NA NA NA NA NA NA NA NA NA 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 NA NA NA
[119] NA NA NA NA NA NA NA NA NA NA 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 12 12 12 12
[178] 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 NA NA NA NA NA NA NA NA NA NA NA NA NA 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
[237] 12 12 12 12 12 12 12 NA NA NA NA NA NA NA NA NA NA NA NA NA 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[296] NA NA NA NA NA NA 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 NA NA NA NA NA NA NA NA NA NA NA NA NA 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
[355] 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 NA NA NA NA NA NA NA NA NA NA NA NA NA 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 NA NA NA NA NA NA NA NA NA NA
[414] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 NA NA NA NA NA NA NA NA NA NA NA NA NA 12 12 12 12 12 12 12 12 12 12 12
[473] 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 12 12 12 12 12 12
[532] 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 NA NA NA NA NA NA NA NA NA NA NA NA NA
> d$TOLD2
[1] 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 12 12 12 12 12 12 12 12 12 12 12 12 12 12
[60] 12 12 12 12 12 NA NA NA NA NA NA NA NA NA NA NA NA NA 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 NA NA NA
[119] NA NA NA NA NA NA NA NA NA NA 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 12 12 12 12
[178] 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 NA NA NA NA NA NA NA NA NA NA NA NA NA 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
[237] 12 12 12 12 12 12 12 NA NA NA NA NA NA NA NA NA NA NA NA NA 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
[296] NA NA NA NA NA NA 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 NA NA NA NA NA NA NA NA NA NA NA NA NA 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
[355] 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 NA NA NA NA NA NA NA NA NA NA NA NA NA 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 NA NA NA NA NA NA NA NA NA NA
[414] NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 NA NA NA NA NA NA NA NA NA NA NA NA NA 12 12 12 12 12 12 12 12 12 12 12
[473] 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 12 12 12 12 12 12
[532] 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 NA NA NA NA NA NA NA NA NA NA NA NA NA
> d$TAU3 <- 24
> d$TOLD3 <- 12
>
> k <- parameter_required("^TAU", names(mct), simplify=FALSE)
> mct[,names(k)]
TAU1 TAU1U TAU2 TAU2U TAU3
1 TAU1 PKPTMU TAU2 PKPTMU TAU3
> k <- parameter_required("^TOLD", names(mct), simplify=FALSE)
> mct[,names(k)]
TOLD1 TOLD1U TOLD2 TOLD2U TOLD3
1 TOLD1 TOLD1U TOLD2 PKPTMU TOLD3
>
> #mct$OPTIMIZEKEL <- 0 # don't optimize
> #mct$OPTIMIZEKEL <- 1 # optimize
> mct$OPTIMIZEKEL
[1] 0
> mct$FLGNOCMAX
[1] 1
the subsequent computation engine call generates an error and crashes
> results_list <- run_computation(data=d, map=mct, flag=flags, parameterset=parameterset)
Error in rep(1, (tau_i - told_i + 1)) : invalid 'times' argument
tc011 M1SS multiple dosing intervals
> mct$OPTIMIZEKEL
[1] 1
> mct$FLGNOCMAX
[1] 1
> results_list <- run_computation(data=d, map=mct, flag=flags, parameterset=parameterset)
unit_conversion : Unit Class 1 (Time) time_col: TMAX1 TMAX2 TMIN1 TMIN2 TLAST TLAG KELTMLO KELTMHI THALF LASTTIME MRTLAST MRTLAST1 MRTLAST2 MRTEVIFO1 MRTEVIFO2 MRTEVIFP1 MRTEVIFP2 TAU1 TAU2 TOLD1 TOLD2 parameters are scaled from HR to HR via scaling factor: 1
unit_conversion : Unit Class 3 (Dose) dose_col: DOSE1 parameters are scaled from MG to MG via scaling factor: 1
unit_conversion : Unit Class 3 (Dose) dose_col: DOSE2 parameters are scaled from MG to MG via scaling factor: 1
unit_conversion : Unit Class 4 (Volume) volume_col: VZFTAU1 VZFTAU2 parameters are scaled from ML to L via scaling factor: 0.001
unit_conversion : Unit Class 5 (Amount/Volume) conc_col: CMAX1 CMAX2 CMIN1 CMIN2 CLAST1 CLAST2 KELC0 CAV1 CAV2 parameters are scaled from UG/ML to NG/ML via scaling factor: 1000
unit_conversion : Unit Class 6 (1/Time) kel_col: KEL parameters are scaled from 1/HR to 1/HR via scaling factor: 1
unit_conversion : Unit Class 7 (Volume/Time) cl_col: CLFTAU1 CLFTAU2 parameters are scaled from ML/HR to L/HR via scaling factor: 0.001
unit_conversion : Unit Class 8: (Amount.Time/Volume) auc_col: AUCALL AUCLASTC AUCLAST1 AUCLAST2 AUCLASTC1 AUCLASTC2 AUCINFO AUCINFOC AUCINFP AUCINFPC AUCTAU1 AUCTAU2 parameters are scaled from UG.HR/ML to NG.HR/ML via scaling factor: 1000
unit_conversion : Unit Class 9: (Amount.Time.Time/Volume) aumc_col: AUMCTAU1 AUMCTAU2 parameters are scaled from UG.HR.HR/ML to NG.HR.HR/ML via scaling factor: 1000
unit_conversion : Unit Class 12: ([Amount/Volume]/Amount) concdn_col: CMAXDN1 CMAXDN2 parameters are scaled from UG/ML/MG to NG/ML/MG via scaling factor: 1000
There were 50 or more warnings (use warnings() to see the first 50)
> mct$FLGNOCMAX
[1] 1
> r <- results_list$data_out
> e <- results_list$est_data
> f <- results_list$flags_data
r
SDEID DOSE1 DOSE2 DOSEC CMAX1 CMAX2 CMAXDN1 CMAXDN2 FLGACCEPTPREDOSE CMIN1 CMIN2 CLAST1 CLAST2 TMAX1 TMAX2 TMIN1 TMIN2 TLAST TLAG KEL KELC0 KELTMLO KELTMHI KELNOPT
1 130509330 300 300 300 8120 9540 27.06667 31.80000 0 1940 2200 2290 2200 1.500 13.000 0 24 24 0 0.10416094 26895.50 18 24 3
2 246409536 300 300 300 8130 12600 27.10000 42.00000 0 945 2240 2240 2420 0.667 12.667 0 12 24 0 0.09888024 25947.24 16 24 4
3 249265153 300 300 300 12200 12100 40.66667 40.33333 0 1620 1720 2030 1720 1.000 13.000 0 24 24 0 0.13200860 40690.22 16 24 4
4 283369224 300 300 300 8060 12900 26.86667 43.00000 0 2060 1990 2520 1990 2.000 12.667 0 24 24 0 0.12773909 43409.40 16 24 4
5 292748221 300 300 300 10900 11500 36.33333 38.33333 0 2260 2490 2720 2490 1.000 12.667 0 24 24 0 0.12117629 44414.40 14 24 5
KELRSQ KELRSQA FLGACCEPTKEL THALF THALFF LASTTIME FLGACCEPTTAU AUCALL AUCLASTC AUCLASTDN AUCLAST1 AUCLAST2 AUCLASTC1 AUCLASTC2 AUCLASTDN1 AUCLASTDN2 AUC1 AUC2 AUC3
1 0.9995933 0.9991866 1 6.654578 1 24 1 109551.6 89397.55 0.3651721 52845.65 56408.31 28884.10 36254.25 0.1761522 0.1880277 0.8341650 2.400625 4.718305
2 0.9990935 0.9986402 1 7.009967 1 24 1 106168.3 95720.65 0.3538942 47738.74 58241.68 35264.26 47794.05 0.1591291 0.1941389 0.9948375 3.192557 5.815717
3 0.9992316 0.9988474 1 5.250773 1 24 1 105527.0 92738.73 0.3517568 52905.14 52810.07 38115.86 40021.75 0.1763505 0.1760336 0.6826500 2.464540 5.859475
4 0.9981222 0.9971833 1 5.426273 1 24 1 114926.6 98048.17 0.3830887 56672.84 57905.26 37064.23 41026.82 0.1889095 0.1930175 0.7242750 2.085325 4.386355
5 0.9974224 0.9965632 1 5.720155 1 24 1 126567.0 106898.69 0.4218900 60777.39 65482.37 37769.98 45814.05 0.2025913 0.2182746 0.8974350 2.729425 5.849635
AUC4 AUC5 AUC6 AUC7 AUC8 AUC9 AUC10 AUC11 AUC12 AUC13 AUC14 AUC15 AUC16 AUC17 AUC18 AUCINT1 AUCINT2 AUCINT3 AUCINT4 AUCINT5 AUCINT6
1 8.650805 12.44749 24.65110 34.14267 44.97227 52.84565 53.61321 55.58214 58.74731 63.26010 67.45218 81.15689 90.81730 101.46176 109.2540 0_0.333 0_0.667 0_1 0_1.5 0_2 0_4
2 9.411433 12.52392 22.40110 30.30788 40.09891 47.73874 49.53361 53.06399 56.62310 60.70804 64.29425 76.44969 86.15488 97.56380 105.9804 0_0.333 0_0.667 0_1 0_1.5 0_2 0_4
3 11.018396 14.95149 27.18914 36.12720 45.83054 52.90514 53.94410 56.29045 59.94346 65.12475 69.18582 81.37289 90.06063 99.44468 105.7152 0_0.333 0_0.667 0_1 0_1.5 0_2 0_4
4 8.333855 12.30635 25.64657 35.66197 47.72318 56.67284 57.89495 60.85419 64.62109 69.16313 73.16058 86.23729 96.12274 107.14399 114.5781 0_0.333 0_0.667 0_1 0_1.5 0_2 0_4
5 10.817479 15.09058 29.06755 39.55078 51.52416 60.77739 62.69380 66.08223 69.79396 74.88027 79.35072 93.96139 105.16892 117.49550 126.2598 0_0.333 0_0.667 0_1 0_1.5 0_2 0_4
AUCINT7 AUCINT8 AUCINT9 AUCINT10 AUCINT11 AUCINT12 AUCINT13 AUCINT14 AUCINT15 AUCINT16 AUCINT17 AUCINT18 AUC0_12 AUC12_24 AUC0_48 AUC16_30 AUC0_10 AUCINFO AUCINFOC AUCINFODN AUCINFP
1 0_6 0_9 0_12 0_12.333 0_12.667 0_13 0_13.5 0_14 0_16 0_18 0_21 0_24 106.28661 112.8166 219.1032 56.19414 90.44661 130672.8 112047.8 0.4355759 130749.8
2 0_6 0_9 0_12 0_12.333 0_12.667 0_13 0_13.5 0_14 0_16 0_18 0_21 0_24 95.85319 116.4834 212.3365 59.06146 80.49320 130642.3 121085.3 0.4354744 130622.1
3 0_6 0_9 0_12 0_12.333 0_12.667 0_13 0_13.5 0_14 0_16 0_18 0_21 0_24 105.81029 105.2438 211.0541 48.68464 91.66109 118556.5 106284.6 0.3951883 118497.4
4 0_6 0_9 0_12 0_12.333 0_12.667 0_13 0_13.5 0_14 0_16 0_18 0_21 0_24 114.04271 115.8105 229.8532 56.68161 95.98271 130505.2 114378.6 0.4350175 130769.2
5 0_6 0_9 0_12 0_12.333 0_12.667 0_13 0_13.5 0_14 0_16 0_18 0_21 0_24 122.16927 130.9647 253.1340 64.59674 103.56927 147115.6 128465.1 0.4903853 146569.2
AUCINFPC AUCINFPDN AUCTAU1 AUCTAU2 AUCTAUDN1 AUCTAUDN2 AUMCTAU1 AUMCTAU2 MRTLAST MRTLAST1 MRTLAST2 MRTEVIFO1 MRTEVIFO2 MRTEVIFP1 MRTEVIFP2 AUCXPCTO AUCXPCTP CAV1 CAV2
1 112124.8 0.4358326 52845.65 56408.31 0.1761522 0.1880277 260105.4 942066.2 10.94530 4.921983 16.70084 224.5912 556.8441 276.3798 680.3965 16.16340 16.21277 4403.804 4700.693
2 121065.1 0.4354069 47738.74 58241.68 0.1591291 0.1941389 234609.1 972113.2 11.34247 4.914439 16.69102 224.4750 469.6765 262.9995 505.0156 18.73363 18.72103 3978.228 4853.473
3 106225.5 0.3949915 52905.14 52810.07 0.1763505 0.1760336 243375.6 865304.0 10.48215 4.600225 16.38521 140.5347 277.5242 170.1054 322.0234 10.99008 10.94572 4408.762 4400.839
4 114642.6 0.4358974 56672.84 57905.26 0.1889095 0.1930175 286599.3 959068.4 10.80928 5.057084 16.56272 370.5493 350.4807 485.4397 413.7246 11.93717 12.11494 4722.737 4825.438
5 127918.7 0.4885640 60777.39 65482.37 0.2025913 0.2182746 296295.7 1086653.1 10.89879 4.875098 16.59459 255.1137 444.1550 303.1944 489.7116 13.96764 13.64693 5064.782 5456.864
CLFTAU1 CLFTAU2 PTF1 PTF2 PTR1 PTR2 VZFTAU1 VZFTAU2 VZFTAUW1 VZFTAUW2 CONC1 CONC2 CONC3 CONC4 CONC5 CONC6 CONC7 CONC8 CONC9 CONC10 CONC11 CONC12 CONC13
1 0.005676910 0.005318365 1.403332 1.561472 4.185567 4.336364 0.05450133 0.05105911 0.7887313 0.7389162 1.940 3.07 6.31 7.61 8.12 7.09 5.21 4.31 2.99 2.29 2.32 9.47 9.54
2 0.006284205 0.005150951 1.806080 2.134554 8.603175 5.625000 0.06355370 0.05209282 0.7570422 0.6205220 0.945 5.03 8.13 7.63 6.77 5.71 4.24 3.68 2.88 2.24 8.54 12.60 8.98
3 0.005670526 0.005680735 2.399767 2.358641 7.530864 7.034884 0.04295573 0.04303307 0.6368530 0.6379995 1.620 2.48 8.19 12.20 8.64 7.14 5.20 3.81 2.72 2.03 4.21 9.84 12.10
4 0.005293541 0.005180877 1.270450 2.260935 3.912621 6.482412 0.04144026 0.04055827 0.5173565 0.5063455 2.060 2.29 5.86 7.96 7.83 8.06 5.45 4.59 3.50 2.52 4.82 12.90 9.86
5 0.004936046 0.004581386 1.705898 1.651132 4.823009 4.618474 0.04073443 0.03780761 0.6634271 0.6157592 2.260 3.13 7.84 10.90 9.03 8.08 6.00 4.55 3.48 2.72 8.79 11.50 10.80
CONC14 CONC15 CONC16 CONC17 CONC18 CONC19 CONCTIME1 CONCTIME2 CONCTIME3 CONCTIME4 CONCTIME5 CONCTIME6 CONCTIME7 CONCTIME8 CONCTIME9 CONCTIME10 CONCTIME11 CONCTIME12 CONCTIME13 CONCTIME14
1 8.53 8.24 5.63 4.11 3.04 2.20 0 0.333 0.667 1 1.5 2 4 6 9 12 12.333 12.667 13 13.5
2 7.41 6.94 5.29 4.44 3.23 2.42 0 0.333 0.667 1 1.5 2 4 6 9 12 12.333 12.667 13 13.5
3 8.80 7.48 4.89 3.84 2.51 1.72 0 0.333 0.667 1 1.5 2 4 6 9 12 12.333 12.667 13 13.5
4 8.35 7.65 5.54 4.39 3.04 1.99 0 0.333 0.667 1 1.5 2 4 6 9 12 12.333 12.667 13 13.5
5 9.57 8.34 6.36 4.91 3.40 2.49 0 0.333 0.667 1 1.5 2 4 6 9 12 12.333 12.667 13 13.5
CONCTIME15 CONCTIME16 CONCTIME17 CONCTIME18 CONCTIME19 DI1 DI2 TAU1 TAU2 TOLD1 TOLD2 TIMEU AMOUNTU DOSE1U DOSE2U VOLUMEU CONCU KELU CLU AUCU AUMCU AUCNORMU AURCU CONCNORMU RATEU
1 14 16 18 21 24 NA NA 12 12 0 12 HR NA MG MG L NG/ML 1/HR L/HR NG.HR/ML NG.HR.HR/ML NA NA NG/ML/MG NA
2 14 16 18 21 24 NA NA 12 12 0 12 HR NA MG MG L NG/ML 1/HR L/HR NG.HR/ML NG.HR.HR/ML NA NA NG/ML/MG NA
3 14 16 18 21 24 NA NA 12 12 0 12 HR NA MG MG L NG/ML 1/HR L/HR NG.HR/ML NG.HR.HR/ML NA NA NG/ML/MG NA
4 14 16 18 21 24 NA NA 12 12 0 12 HR NA MG MG L NG/ML 1/HR L/HR NG.HR/ML NG.HR.HR/ML NA NA NG/ML/MG NA
5 14 16 18 21 24 NA NA 12 12 0 12 HR NA MG MG L NG/ML 1/HR L/HR NG.HR/ML NG.HR.HR/ML NA NA NG/ML/MG NA
VOLUMEWU CLWU STUDY SITEID SUBJID RAND TREATXT TRTCD PKCOLL PKBDFLD PKTERM PERIODU PERIOD VISITU VISIT ACTTRT ACTTRTC
1 NA NA A0081216 1001 10011015 15 1X300 mg IR Fasted q12h B POINT PLASMA PREGABALIN PERIOD 2 DAY 4 PREGABALIN 1 X 300 MG IR CAPSULE FASTED B
2 NA NA A0081216 1001 10011008 8 1X300 mg IR Fasted q12h B POINT PLASMA PREGABALIN PERIOD 1 DAY 4 PREGABALIN 1 X 300 MG IR CAPSULE FASTED B
3 NA NA A0081216 1001 10011007 7 1X300 mg IR Fasted q12h B POINT PLASMA PREGABALIN PERIOD 2 DAY 4 PREGABALIN 1 X 300 MG IR CAPSULE FASTED B
4 NA NA A0081216 1001 10011005 5 1X300 mg IR Fasted q12h B POINT PLASMA PREGABALIN PERIOD 1 DAY 4 PREGABALIN 1 X 300 MG IR CAPSULE FASTED B
5 NA NA A0081216 1001 10011010 10 1X300 mg IR Fasted q12h B POINT PLASMA PREGABALIN PERIOD 2 DAY 4 PREGABALIN 1 X 300 MG IR CAPSULE FASTED B
DOSEP DOSPND ROUTE RACES SEX HT WT HTUNI WTUNI TAU
1 PREGABALIN BD DONE ORAL WHITE M 172 69.10 CM KG 12
2 PREGABALIN BD DONE ORAL WHITE M 185 83.95 CM KG 12
3 PREGABALIN BD DONE ORAL BLACK M 171 67.45 CM KG 12
4 PREGABALIN BD DONE ORAL WHITE M 175 80.10 CM KG 12
5 PREGABALIN BD DONE ORAL WHITE M 180 61.40 CM KG 12
> e
PKDATAROWID SDEID TIME CEST_KEL CEST_INT CEST_EXT CEST_C0 CEST_TLAST
1 26908935 70745987 18.0 4.780859 NA NA NA NA
2 26908936 70745987 21.0 3.498742 NA NA NA NA
3 26908937 70745987 24.0 2.560460 NA NA NA 2.560460
4 26908954 130509330 18.0 4.124984 NA NA NA NA
5 26908955 130509330 21.0 3.017955 NA NA NA NA
6 26908956 130509330 24.0 2.208021 NA NA NA 2.208021
7 26908535 246409536 16.0 5.333360 NA NA NA NA
8 26908536 246409536 18.0 4.376376 NA NA NA NA
9 26908537 246409536 21.0 3.253009 NA NA NA NA
10 26908538 246409536 24.0 2.417997 NA NA NA 2.417997
11 26908877 249265153 16.0 4.922666 NA NA NA NA
12 26908878 249265153 18.0 3.780412 NA NA NA NA
13 26908879 249265153 21.0 2.544177 NA NA NA NA
14 26908880 249265153 24.0 1.712204 NA NA NA 1.712204
15 26908516 283369224 16.0 5.622917 NA NA NA NA
16 26908517 283369224 18.0 4.355208 NA NA NA NA
17 26908518 283369224 21.0 2.968792 NA NA NA NA
18 26908519 283369224 24.0 2.023721 NA NA NA 2.023721
19 26908895 292748221 14.0 8.142487 NA NA NA NA
20 26908896 292748221 16.0 6.390056 NA NA NA NA
21 26908897 292748221 18.0 5.014785 NA NA NA NA
22 26908898 292748221 21.0 3.486372 NA NA NA NA
23 26908899 292748221 24.0 2.423791 NA NA NA 2.423791
24 26908495 328228072 13.5 6.605181 NA NA NA NA
25 26908496 328228072 14.0 6.128861 NA NA NA NA
26 26908497 328228072 16.0 4.543182 NA NA NA NA
27 26908498 328228072 18.0 3.367756 NA NA NA NA
28 26908499 328228072 21.0 2.149370 NA NA NA NA
29 26908500 328228072 24.0 1.371771 NA NA NA 1.371771
30 26908818 331625416 13.5 9.835481 NA NA NA NA
31 26908819 331625416 14.0 9.241418 NA NA NA NA
32 26908820 331625416 16.0 7.202953 NA NA NA NA
33 26908821 331625416 18.0 5.614131 NA NA NA NA
34 26908822 331625416 21.0 3.863140 NA NA NA NA
35 26908823 331625416 24.0 2.658266 NA NA NA 2.658266
36 26908839 494439910 16.0 5.601851 NA NA NA NA
37 26908840 494439910 18.0 4.508992 NA NA NA NA
38 26908841 494439910 21.0 3.256126 NA NA NA NA
39 26908842 494439910 24.0 2.351381 NA NA NA 2.351381
40 26908969 543244472 13.0 8.540985 NA NA NA NA
41 26908970 543244472 13.5 8.080628 NA NA NA NA
42 26908971 543244472 14.0 7.645084 NA NA NA NA
43 26908972 543244472 16.0 6.125348 NA NA NA NA
44 26908973 543244472 18.0 4.907714 NA NA NA NA
45 26908974 543244472 21.0 3.519670 NA NA NA NA
46 26908975 543244472 24.0 2.524206 NA NA NA 2.524206
47 26908572 695874499 14.0 7.097609 NA NA NA NA
48 26908573 695874499 16.0 5.476251 NA NA NA NA
49 26908574 695874499 18.0 4.225272 NA NA NA NA
50 26908575 695874499 21.0 2.863595 NA NA NA NA
51 26908576 695874499 24.0 1.940745 NA NA NA 1.940745
52 26908476 783385801 13.5 8.322697 NA NA NA NA
53 26908477 783385801 14.0 7.852061 NA NA NA NA
54 26908478 783385801 16.0 6.221025 NA NA NA NA
55 26908479 783385801 18.0 4.928789 NA NA NA NA
56 26908480 783385801 21.0 3.475823 NA NA NA NA
57 26908481 783385801 24.0 2.451179 NA NA NA 2.451179
58 26908553 797620983 14.0 9.083895 NA NA NA NA
59 26908554 797620983 16.0 7.349303 NA NA NA NA
60 26908555 797620983 18.0 5.945936 NA NA NA NA
61 26908556 797620983 21.0 4.326944 NA NA NA NA
62 26908557 797620983 24.0 3.148780 NA NA NA 3.148780
63 26908916 856727921 18.0 6.093222 NA NA NA NA
64 26908917 856727921 21.0 4.251489 NA NA NA NA
65 26908918 856727921 24.0 2.966437 NA NA NA 2.966437
66 26908858 895085608 16.0 6.810659 NA NA NA NA
67 26908859 895085608 18.0 5.571924 NA NA NA NA
68 26908860 895085608 21.0 4.123151 NA NA NA NA
69 26908861 895085608 24.0 3.051078 NA NA NA 3.051078
70 26908610 897885073 14.0 8.898941 NA NA NA NA
71 26908611 897885073 16.0 7.213563 NA NA NA NA
72 26908612 897885073 18.0 5.847380 NA NA NA NA
73 26908613 897885073 21.0 4.267550 NA NA NA NA
74 26908614 897885073 24.0 3.114554 NA NA NA 3.114554
75 26908593 916516839 18.0 5.022101 NA NA NA NA
76 26908594 916516839 21.0 3.451510 NA NA NA NA
77 26908595 916516839 24.0 2.372099 NA NA NA 2.372099
78 26908629 982849649 14.0 7.872877 NA NA NA NA
79 26908630 982849649 16.0 6.229732 NA NA NA NA
80 26908631 982849649 18.0 4.929528 NA NA NA NA
81 26908632 982849649 21.0 3.469838 NA NA NA NA
82 26908633 982849649 24.0 2.442379 NA NA NA 2.442379
> f
NULL
Note that estimated concentration dataset missing any entries required for interpolation/extrapolation as reported in issue #2
When using tc012 and using the value of "PARAMETERDISPLAYLIST" for parameterset the computation engine crashes. This does not cause an error for tc010, tc011, tc013 or tc014. Errors for tc015 and tc016 are documented separately due to their unique error messages.
parameterset <- "PARAMETERDISPLAYLIST"
Error in `[.data.frame`(computation_df, , flag_df$VAR[f]) :
undefined columns selected
No option for traceback is provided for this error.
The latest commit, 1fc479c, has an error when running computation while asking for partial AUC. The following error is given when running test case tc012, tc014 and tc016 when a partial AUC is added to the mct:
Error in value[[3L]](cond) :
Error in opt_list[3] %in% names(map_data): object 'opt_list' not found
All three test cases worked for the base test cases with no changes; error only occurs when including a partial AUC. While the traceback is similar for each test case, I've included them all below along with a reproducible example for test case 012.
Traceback for tc012 (with added partial AUC)
Error in value[[3L]](cond) :
Error in opt_list[3] %in% names(map_data): object 'opt_list' not found
For SDEID 24280050
7. stop(paste0(e, "For SDEID ", unique(data_data[, map_data$SDEID])[i])) at
run_M1_SS_computation.R#2601
6. value[[3L]](cond)
5. tryCatchOne(expr, names, parentenv, handlers[[1L]])
4. tryCatchList(expr, classes, parentenv, handlers)
3. tryCatch({
if (comp_required[["CMAXi"]]) {
c_maxi <- list()
} ... at
run_M1_SS_computation.R#1021
2. run_M1_SS_computation(data = merged_data, map = map_data, method = method,
model_regex = model_regex, parameter_list = parameter_list,
return_list = return_list) at
run_computation.R#2223
1. run_computation(data = d, map = mct, flag = flags, parameterset = parameterset)
Traceback for tc014 (with added partial AUC)
Error in value[[3L]](cond) :
Error in opt_list[3] %in% names(map_data): object 'opt_list' not found
For SDEID 24280050
7. stop(paste0(e, "For SDEID ", unique(data_data[, map_data$SDEID])[i])) at
run_M2_SS_computation.R#2291
6. value[[3L]](cond)
5. tryCatchOne(expr, names, parentenv, handlers[[1L]])
4. tryCatchList(expr, classes, parentenv, handlers)
3. tryCatch({
if (comp_required[["C0"]]) {
c_0 <- list()
} ... at
run_M2_SS_computation.R#935
2. run_M2_SS_computation(data = merged_data, map = map_data, method = method,
model_regex = model_regex, parameter_list = parameter_list,
return_list = return_list) at
run_computation.R#2235
1. run_computation(data = d, map = mct, flag = flags, parameterset = parameterset)
Traceback for tc016 (with added partial AUC)
Error in value[[3L]](cond) :
Error in opt_list[3] %in% names(map_data): object 'opt_list' not found
For SDEID 24280050
7. stop(paste0(e, "For SDEID ", unique(data_data[, map_data$SDEID])[i])) at
run_M3_SS_computation.R#2174
6. value[[3L]](cond)
5. tryCatchOne(expr, names, parentenv, handlers[[1L]])
4. tryCatchList(expr, classes, parentenv, handlers)
3. tryCatch({
if (comp_required[["CMAXi"]]) {
c_maxi <- list()
} ... at
run_M3_SS_computation.R#922
2. run_M3_SS_computation(data = merged_data, map = map_data, method = method,
model_regex = model_regex, parameter_list = parameter_list,
return_list = return_list) at
run_computation.R#2247
1. run_computation(data = d, map = mct, flag = flags, parameterset = parameterset)
#### Load Libraries ------------------------------------------------------ ####
library(readr)
library(jsonlite)
library(testthat)
#### Load specific code-base for OpenNCA --------------------------------- ####
# pkgload::load_all(path = "codebase/openNCA-master/openNCA")
# pkgload::load_all(path = "codebase/openNCA-b1977923fc/openNCA") # 11/8-v1
# pkgload::load_all(path = "codebase/openNCA-051805c887/openNCA") # 11/8-v2
pkgload::load_all(path = "codebase/openNCA-1fc479c2ca/openNCA") # 11/11
#### Clean test environment ---------------------------------------------- ####
# Clean environment
rm(params, conc, vlist, results, results_list, r, e, mct, d, flags, p, klist, n,
ninc, nexc, k, rlist, dlist, nlist, slist, df, xmin, xmax, dk, flag_data,
est_data, outputdir)
profileid <- "SDEID"
args = commandArgs(trailingOnly=TRUE)
params = c()
#### Test Case 012 ------------------------------------------------------- ####
# M1 SS
# p <- "C:/Users/tensfeldt/Box Sync/EQuIP Computation Engine/Rudraya_executed_test_cases/tc012"
testcase.id <- "tc012"
p <- "testcase/tc012_M1SS"
params["INPUT1"] = file.path(p, "A0081216.in.csv")
params["parameterset"] ="PARAMETERLIST"
params["map"] = file.path(p, "tc012_mct_updated.csv")
params["flags"] = file.path(p, "A0081216_flag_data.in.csv")
params["OUTPUT"] ="."
print(params)
mct <- read_csv(params["map"])
write_file(toJSON(mct, na="string"), path=file.path(p, "tc012_mct_updated.json"))
params["map"] = file.path(p, "tc012_mct_updated.json")
flags <- read_csv(params["flags"])
write_file(toJSON(flags, na="string"), path=file.path(p, "tc012_flags.json"))
params["flags"] = file.path(p, "tc012_flags.json")
#### Read full concentration dataset ------------------------------------- ####
# Read conc
conc <- read.csv(file=params["INPUT1"], header=TRUE, sep=",", as.is=TRUE)
vlist <- c("PKDATAROWID", "PKPTMS", "PKCNCN", "PERIOD", "TREATXT")
# Read mct
mct <- params["map"]
###mct <- read_file(mct)
###mct <- gsub("[\\]+", "", mct)
mct <- fromJSON(mct)
# Read flags
flags <- params["flags"]
###flags <- read_file(flags)
###flags <- gsub("[\\]+", "", flags)
flags <- fromJSON(flags)
# ParameterSet
if(is.na(params["parameterset"])) { params["parameterset"] <- "PARAMETERDISPLAYLIST" }
parameterset <- params["parameterset"]
# FLAGS is a subset for the specific profile, subset concentration dataframe
# to those records by PKDATAROWID
d <- conc[is.element(conc$PKDATAROWID, flags$PKDATAROWID),]
# Add in missing required parameters for virtual compute call
klist <- c("KEL", "KELRSQ", "KELNOPT", "THALFF")
if(!is.element("PARAMETERDISPLAYLIST", names(mct))) { mct$PARAMETERDISPLAYLIST <- "" }
for(i in klist) {
###if(regexpr(i, mct$PARAMETERDISPLAYLIST, ignore.case=TRUE)==-1) { mct$PARAMETERDISPLAYLIST <- c(mct$PARAMETERDISPLAYLIST, i) }
if(regexpr(i, mct$PARAMETERDISPLAYLIST, ignore.case=TRUE)==-1) { mct$PARAMETERDISPLAYLIST <- paste(mct$PARAMETERDISPLAYLIST, i, sep=";") }
}
# Remove any leading semi-colons ";"
mct$PARAMETERDISPLAYLIST <- gsub("^;*", "", mct$PARAMETERDISPLAYLIST, ignore.case=TRUE, perl=TRUE)
# For virtual compute call only: Subset the parameters to be calculated to those in parameterdisplaylist
if(!is.element("PARAMETERLIST", names(mct))) { mct$PARAMETERLIST <- "" }
# Check whether TIME selection has been set in MCT - default to NOMINAL
if(!is.element("TIME", names(mct))) { mct$TIME <- "NOMINAL" }
# Determine whether there are any points selected for inclusion in the terminal elimination phase via
# FLGEXKEL (0=not excluded/included; 1=excluded/not included)
# ***What happens if there are no flags provided?***
n <- factor(flags$FLGEXKEL, levels=c(0,1))
ninc <- length(n[n==0])
nexc <- length(n[n==1])
#### Final changes to test ----------------------------------------------- ####
### Add partial AUC
mct$AUC.1.T1 <- 11
mct$AUC.1.T2 <- 26
#### Run computation engine ---------------------------------------------- ####
# Run computation
results_list <- run_computation(data = d, map = mct, flag = flags,
parameterset = parameterset)
When running a test case with a partial AUC including a point beyond the final observed value, the extrapolated point does not appear in the est_data output. Despite this, extrapolated points do seem to affect the value given in data_out for the partial AUC.
Using tc010 with two added partial AUCs from 0 to 120 and from 0 to 144. The first partial AUC includes extrapolation for some SDEIDs, while other SDEIDs have an observed value at this time. No SDEIDs have an observed value at 144.
The output from est_data is:
PKDATAROWID SDEID TIME CEST_KEL CEST_INT CEST_EXT CEST_C0 CEST_TLAST
1 NA 2925168 7 NA 3.59500000 NA NA NA
2 22289754 2925168 24 1.7026376 NA NA NA NA
3 22289755 2925168 48 0.5878072 NA NA NA NA
4 NA 2925168 64 NA 0.32866667 NA NA NA
5 22289756 2925168 72 0.2029306 NA NA NA 0.2029306
6 NA 40876220 7 NA 1.11575000 NA NA NA
7 22289571 40876220 16 0.4954443 NA NA NA NA
8 22289572 40876220 24 0.3651747 NA NA NA NA
9 22289573 40876220 48 0.1462239 NA NA NA NA
10 NA 40876220 64 NA 0.08246667 NA NA NA
The extrapolated points do not appear in the est_data, while the interpolated points (from partial AUCs 0 to 7 and 0 to 64 from the original tc010) do appear.
The output from data_out for the same run is:
SDEID AUC0_7 AUC8_48 AUC0_64 AUC0_120 AUC0_144
1 111299859 5.15075 16.11586 23.59851 27.81464 29.81102
2 112146587 16.63043 27.18657 47.51955 50.43985 51.81112
3 123811839 1826.88593 1140.50993 3155.39210 3185.37920 3189.62671
4 145593586 759.77223 1482.00641 2543.66350 2692.11817 2708.33439
5 202367199 6447.26053 5524.24397 12915.26200 13167.20305 13196.88021
6 233891605 2026.37365 1432.19220 3683.11590 3729.02670 3733.35383
When comparing the partial AUCs given in data_out a difference between AUC0_120 and AUC0_144 is observed. This implies that extrapolation is likely occurring and being used to calculate the partial AUCs, but aren't being reported in est_data. This is consistent for all SDEIDs (including 2925168 shown in the est_data above).
#### Load Libraries ------------------------------------------------------ ####
library(readr)
library(jsonlite)
library(testthat)
#### Load specific code-base for OpenNCA --------------------------------- ####
# pkgload::load_all(path = "codebase/openNCA-master/openNCA")
# pkgload::load_all(path = "codebase/openNCA-b1977923fc/openNCA") # 11/8-v1
# pkgload::load_all(path = "codebase/openNCA-051805c887/openNCA") # 11/8-v2
# pkgload::load_all(path = "codebase/openNCA-1fc479c2ca/openNCA") # 11/11
# pkgload::load_all(path = "codebase/openNCA-a06c1c7461/openNCA") # 11/12
pkgload::load_all(path = "codebase/openNCA-e3be44d927/openNCA") # 11/13
#### Clean test environment ---------------------------------------------- ####
# Clean environment
rm(params, conc, vlist, results, results_list, r, e, mct, d, flags, p, klist, n,
ninc, nexc, k, rlist, dlist, nlist, slist, df, xmin, xmax, dk, flag_data,
est_data, outputdir)
profileid <- "SDEID"
args = commandArgs(trailingOnly=TRUE)
params = c()
#### Test Case 010 ------------------------------------------------------- ####
# M1 SD
# p <- "C:/Users/tensfeldt/Box Sync/EQuIP Computation Engine/Rudraya_executed_test_cases/tc010"
testcase.id <- "tc010"
p <- "testcase/tc010_M1SD"
params["INPUT1"] = file.path(p, "B1261002.in.csv")
params["parameterset"] ="PARAMETERLIST"
params["map"] = file.path(p, "tc010_mct_updated.csv")
params["flags"] = file.path(p, "B1261002_flag_data.in.csv")
params["OUTPUT"] ="."
print(params)
mct <- read_csv(params["map"])
write_file(toJSON(mct, na="string"), path=file.path(p, "tc010_mct_updated.json"))
params["map"] = file.path(p, "tc010_mct_updated.json")
flags <- read_csv(params["flags"])
write_file(toJSON(flags, na="string"), path=file.path(p, "tc010_flags.json"))
params["flags"] = file.path(p, "tc010_flags.json")
#### Read full concentration dataset ------------------------------------- ####
# Read conc
conc <- read.csv(file=params["INPUT1"], header=TRUE, sep=",", as.is=TRUE)
vlist <- c("PKDATAROWID", "PKPTMS", "PKCNCN", "PERIOD", "TREATXT")
# Read mct
mct <- params["map"]
###mct <- read_file(mct)
###mct <- gsub("[\\]+", "", mct)
mct <- fromJSON(mct)
# Read flags
flags <- params["flags"]
###flags <- read_file(flags)
###flags <- gsub("[\\]+", "", flags)
flags <- fromJSON(flags)
# ParameterSet
if(is.na(params["parameterset"])) { params["parameterset"] <- "PARAMETERDISPLAYLIST" }
parameterset <- params["parameterset"]
# FLAGS is a subset for the specific profile, subset concentration dataframe
# to those records by PKDATAROWID
d <- conc[is.element(conc$PKDATAROWID, flags$PKDATAROWID),]
# Add in missing required parameters for virtual compute call
klist <- c("KEL", "KELRSQ", "KELNOPT", "THALFF")
if(!is.element("PARAMETERDISPLAYLIST", names(mct))) { mct$PARAMETERDISPLAYLIST <- "" }
for(i in klist) {
###if(regexpr(i, mct$PARAMETERDISPLAYLIST, ignore.case=TRUE)==-1) { mct$PARAMETERDISPLAYLIST <- c(mct$PARAMETERDISPLAYLIST, i) }
if(regexpr(i, mct$PARAMETERDISPLAYLIST, ignore.case=TRUE)==-1) { mct$PARAMETERDISPLAYLIST <- paste(mct$PARAMETERDISPLAYLIST, i, sep=";") }
}
# Remove any leading semi-colons ";"
mct$PARAMETERDISPLAYLIST <- gsub("^;*", "", mct$PARAMETERDISPLAYLIST, ignore.case=TRUE, perl=TRUE)
# For virtual compute call only: Subset the parameters to be calculated to those in parameterdisplaylist
if(!is.element("PARAMETERLIST", names(mct))) { mct$PARAMETERLIST <- "" }
# Check whether TIME selection has been set in MCT - default to NOMINAL
if(!is.element("TIME", names(mct))) { mct$TIME <- "NOMINAL" }
# Determine whether there are any points selected for inclusion in the terminal elimination phase via
# FLGEXKEL (0=not excluded/included; 1=excluded/not included)
# ***What happens if there are no flags provided?***
n <- factor(flags$FLGEXKEL, levels=c(0,1))
ninc <- length(n[n==0])
nexc <- length(n[n==1])
#### Final changes to test ----------------------------------------------- ####
### Add partial AUC
mct$AUC.4.T1 <- 0
mct$AUC.4.T2 <- 120
mct$AUC.5.T1 <- 0
mct$AUC.5.T2 <- 144
#### Run computation engine ---------------------------------------------- ####
### Run computation
results_list <- run_computation(data = d, map = mct, flag = flags,
parameterset = parameterset)
### Save results to objects
r <- results_list$data_out
e <- results_list$est_data
f <- results_list$flag_data
### View output
head(e, 10)
e[e$TIME == 144,] # no rows with time 144
head(r[c("SDEID", "AUC0_7", "AUC8_48", "AUC0_64", "AUC0_120", "AUC0_144")])
r$AUC0_144 - r$AUC0_120 # partial AUC 0-120 consistently smaller than 0-144
Something has changes drastically with out the map_data is being evaluated relative to the master branch. No change has occurred to tc010 configuration. Please execute tc010 and provide results and review and resolve ASAP.
results_list <- run_computation(data=d, map=mct, flag=flags, parameterset=parameterset)
Error in value[3L] :
Error in[.data.frame(map_data, , opt_list[3]): undefined columns selected
For SDEID 2925168
In addition: Warning messages:
1: In run_M1_SD_computation(data = merged_data, map = map_data, method = method, :
Flag 'FLGACCEPTTAU' cannot be computed if 'TAUi' is not provided
2: In run_M1_SD_computation(data = merged_data, map = map_data, method = method, :
Show Traceback
Rerun with Debug
Error in value[3L] :
Error in [.data.frame(map_data, , opt_list[3]): undefined columns selected
For SDEID 2925168
stop(paste0(e, "For SDEID ", unique(data_data[, map_data$SDEID])[i])) at run_M1_SD_computation.R#1624
6.
value[3L]
5.
tryCatchOne(expr, names, parentenv, handlers[[1L]])
4.
tryCatchList(expr, classes, parentenv, handlers)
3.
tryCatch({
tmp_df <- data_data[data_data[, map_data$SDEID] == unique(data_data[,
map_data$SDEID])[i], ]
tmp_df <- tmp_df[order(tmp_df[, map_data$TIME]), ] ... at run_M1_SD_computation.R#812
2.
run_M1_SD_computation(data = merged_data, map = map_data, method = method,
model_regex = model_regex, parameter_list = parameter_list,
return_list = return_list) at run_computation.R#2217
1.
run_computation(data = d, map = mct, flag = flags, parameterset = parameterset)
When using tc016 and using the value of "PARAMETERDISPLAYLIST" for parameterset the computation engine crashes. This does not cause an error for tc010, tc011, tc013 or tc014. Errors for tc012 and tc015 are documented separately due to their unique error messages.
parameterset <- "PARAMETERDISPLAYLIST"
Error in value[[3L]](cond) :
Error in cltau(auctau = auctau[[d]], dose = dose_c[d]): object 'dose_c' not found
For SDEID 12596809
7. stop(paste0(e, "For SDEID ", unique(data_data[, map_data$SDEID])[i])) at run_M3_SS_computation.R#2180
6. value[[3L]](cond)
5. tryCatchOne(expr, names, parentenv, handlers[[1L]])
4. tryCatchList(expr, classes, parentenv, handlers)
3. tryCatch({
if (comp_required[["CMAXi"]]) {
c_maxi <- list()
} ... at run_M3_SS_computation.R#923
2. run_M3_SS_computation(data = merged_data, map = map_data, method = method,
model_regex = model_regex, parameter_list = parameter_list,
return_list = return_list) at run_computation.R#2247
1. run_computation(data = d, map = mct, flag = flags, parameterset = parameterset)
The output for FLGACCEPTKEL does not appear to provide the correct output.
When running the base test case 010 with OPTIMIZEKEL <- 1 the following output is given.
SDEID KEL KELTMLO KELTMHI KELNOPT KELRSQ AUCXPCTP FLGACCEPTKEL
1 111299859 0.05815238 12 48 4 0.9947858 6.81888591 FALSE
2 112146587 0.08695843 8 48 5 0.9906898 1.83937140 FALSE
3 123811839 0.06147205 2 120 10 0.9436264 0.05578474 FALSE
4 145593586 0.04258080 4 120 9 0.9882692 0.58769593 FALSE
5 202367199 0.05424527 4 120 9 0.9623526 0.13064667 TRUE
6 233891605 0.05989964 4 120 9 0.9674385 0.06301853 TRUE
This results from the default value for FLGACCEPTKELCRIT from the mct which is:
[1] "KELRSQ>=0.9,KELNOPT>=3,AUCXPCTP<=0.2"
@tensfeldt - This criteria for AUCXPCTP reads as being less than 0.2%. Is this an error in the test case mcts? Should it be "KELRSQ>=0.9,KELNOPT>=3,AUCXPCTP<=20.0"? This value for FLGACCEPTKELCRIT is consistent across all test cases.
When changing the value using to 20%, the following output is given:
SDEID KEL KELTMLO KELTMHI KELNOPT KELRSQ AUCXPCTP FLGACCEPTKEL
1 111299859 0.05815238 12 48 4 0.9947858 6.81888591 FALSE
2 112146587 0.08695843 8 48 5 0.9906898 1.83937140 TRUE
3 123811839 0.06147205 2 120 10 0.9436264 0.05578474 FALSE
4 145593586 0.04258080 4 120 9 0.9882692 0.58769593 TRUE
5 202367199 0.05424527 4 120 9 0.9623526 0.13064667 TRUE
6 233891605 0.05989964 4 120 9 0.9674385 0.06301853 TRUE
My observations on these results are:
mct$FLGACCEPTKELCRIT does not affect the OPTIMIZEKEL algorithm, which may be intentionalmct$FLGACCEPTKELCRIT interacts strangely with FLGACCEPTKELmct$FLGACCEPTKELCRIT as SDEID == 111299859 should be TRUE#### Load Libraries ------------------------------------------------------ ####
library(readr)
library(jsonlite)
library(testthat)
#### Load specific code-base for OpenNCA --------------------------------- ####
# pkgload::load_all(path = "codebase/openNCA-master/openNCA")
# pkgload::load_all(path = "codebase/openNCA-b1977923fc/openNCA") # 11/8-v1
# pkgload::load_all(path = "codebase/openNCA-051805c887/openNCA") # 11/8-v2
# pkgload::load_all(path = "codebase/openNCA-1fc479c2ca/openNCA") # 11/11
# pkgload::load_all(path = "codebase/openNCA-a06c1c7461/openNCA") # 11/12
pkgload::load_all(path = "codebase/openNCA-e3be44d927/openNCA") # 11/13
#### Clean test environment ---------------------------------------------- ####
# Clean environment
rm(params, conc, vlist, results, results_list, r, e, mct, d, flags, p, klist, n,
ninc, nexc, k, rlist, dlist, nlist, slist, df, xmin, xmax, dk, flag_data,
est_data, outputdir)
profileid <- "SDEID"
args = commandArgs(trailingOnly=TRUE)
params = c()
#### Test Case 010 ------------------------------------------------------- ####
# M1 SD
# p <- "C:/Users/tensfeldt/Box Sync/EQuIP Computation Engine/Rudraya_executed_test_cases/tc010"
testcase.id <- "tc010"
p <- "testcase/tc010_M1SD"
params["INPUT1"] = file.path(p, "B1261002.in.csv")
params["parameterset"] ="PARAMETERLIST"
params["map"] = file.path(p, "tc010_mct_updated.csv")
params["flags"] = file.path(p, "B1261002_flag_data.in.csv")
params["OUTPUT"] ="."
print(params)
mct <- read_csv(params["map"])
write_file(toJSON(mct, na="string"), path=file.path(p, "tc010_mct_updated.json"))
params["map"] = file.path(p, "tc010_mct_updated.json")
flags <- read_csv(params["flags"])
write_file(toJSON(flags, na="string"), path=file.path(p, "tc010_flags.json"))
params["flags"] = file.path(p, "tc010_flags.json")
#### Read full concentration dataset ------------------------------------- ####
# Read conc
conc <- read.csv(file=params["INPUT1"], header=TRUE, sep=",", as.is=TRUE)
vlist <- c("PKDATAROWID", "PKPTMS", "PKCNCN", "PERIOD", "TREATXT")
# Read mct
mct <- params["map"]
###mct <- read_file(mct)
###mct <- gsub("[\\]+", "", mct)
mct <- fromJSON(mct)
# Read flags
flags <- params["flags"]
###flags <- read_file(flags)
###flags <- gsub("[\\]+", "", flags)
flags <- fromJSON(flags)
# ParameterSet
if(is.na(params["parameterset"])) { params["parameterset"] <- "PARAMETERDISPLAYLIST" }
parameterset <- params["parameterset"]
# FLAGS is a subset for the specific profile, subset concentration dataframe
# to those records by PKDATAROWID
d <- conc[is.element(conc$PKDATAROWID, flags$PKDATAROWID),]
# Add in missing required parameters for virtual compute call
klist <- c("KEL", "KELRSQ", "KELNOPT", "THALFF")
if(!is.element("PARAMETERDISPLAYLIST", names(mct))) { mct$PARAMETERDISPLAYLIST <- "" }
for(i in klist) {
###if(regexpr(i, mct$PARAMETERDISPLAYLIST, ignore.case=TRUE)==-1) { mct$PARAMETERDISPLAYLIST <- c(mct$PARAMETERDISPLAYLIST, i) }
if(regexpr(i, mct$PARAMETERDISPLAYLIST, ignore.case=TRUE)==-1) { mct$PARAMETERDISPLAYLIST <- paste(mct$PARAMETERDISPLAYLIST, i, sep=";") }
}
# Remove any leading semi-colons ";"
mct$PARAMETERDISPLAYLIST <- gsub("^;*", "", mct$PARAMETERDISPLAYLIST, ignore.case=TRUE, perl=TRUE)
# For virtual compute call only: Subset the parameters to be calculated to those in parameterdisplaylist
if(!is.element("PARAMETERLIST", names(mct))) { mct$PARAMETERLIST <- "" }
# Check whether TIME selection has been set in MCT - default to NOMINAL
if(!is.element("TIME", names(mct))) { mct$TIME <- "NOMINAL" }
# Determine whether there are any points selected for inclusion in the terminal elimination phase via
# FLGEXKEL (0=not excluded/included; 1=excluded/not included)
# ***What happens if there are no flags provided?***
n <- factor(flags$FLGEXKEL, levels=c(0,1))
ninc <- length(n[n==0])
nexc <- length(n[n==1])
#### Final changes to test ----------------------------------------------- ####
### Optimise Kel
# mct$OPTIMIZEKEL <- 0 # don't optimise
mct$OPTIMIZEKEL <- 1 # optimise
### Check FLGACCEPTKELCRIT
print(mct$FLGACCEPTKELCRIT)
# mct$FLGACCEPTKELCRIT <- "KELRSQ>=0.9,KELNOPT>=3,AUCXPCTP<=0.2"
mct$FLGACCEPTKELCRIT <- "KELRSQ>=0.9,KELNOPT>=3,AUCXPCTP<=20"
#### Run computation engine ---------------------------------------------- ####
### Run computation
results_list <- run_computation(data = d, map = mct, flag = flags,
parameterset = parameterset)
### Save results to objects
r <- results_list$data_out
e <- results_list$est_data
f <- results_list$flag_data
### View results
head(r[c("SDEID", "KEL", "KELTMLO", "KELTMHI", "KELNOPT", "KELRSQ", "AUCXPCTP", "FLGACCEPTKEL")])
When using OPTIMIZEKEL there are cases where an NA value is returned for KEL and its associated outputs.
When using tc012 with mct$OPTIMIZEKEL <- 1 some rows for KEL are given an NA output:
SDEID KEL KELTMLO KELTMHI KELNOPT KELRSQ AUCXPCTP FLGACCEPTKEL
1 130509330 0.13135136 13.5 24 6 0.9846997 12.58249 FALSE
2 147299665 NA NA NA 0 NA NA FALSE
3 24280050 0.11779122 12.0 24 3 0.9970626 10.56075 FALSE
4 246409536 0.11237648 13.0 24 7 0.9828765 16.29267 FALSE
5 246953363 NA NA NA 0 NA NA FALSE
6 249265153 0.03964688 1.5 24 15 0.1861215 41.69759 FALSE
This is compared to the following output when mct$OPTIMIZEKEL <- 0
SDEID KEL KELTMLO KELTMHI KELNOPT KELRSQ AUCXPCTP FLGACCEPTKEL
1 130509330 0.10416094 18 24 3 0.9995933 16.212771 FALSE
2 147299665 0.12611367 12 24 3 0.9999997 9.352225 FALSE
3 24280050 0.11779122 12 24 3 0.9970626 10.560748 FALSE
4 246409536 0.09888024 16 24 4 0.9990935 18.721028 FALSE
5 246953363 0.10647171 12 24 3 0.9975960 14.288932 FALSE
6 249265153 0.13200860 16 24 4 0.9992316 10.945719 FALSE
Despite there being a selection of points that result in an acceptable KELNOPT, KELRSQ and AUCXPCTP value (as provided by a user), it appears that the OPTIMIZEKEL algorithm was unable to find one.
#### Load Libraries ------------------------------------------------------ ####
library(readr)
library(jsonlite)
library(testthat)
#### Load specific code-base for OpenNCA --------------------------------- ####
# pkgload::load_all(path = "codebase/openNCA-master/openNCA")
# pkgload::load_all(path = "codebase/openNCA-b1977923fc/openNCA") # 11/8-v1
# pkgload::load_all(path = "codebase/openNCA-051805c887/openNCA") # 11/8-v2
# pkgload::load_all(path = "codebase/openNCA-1fc479c2ca/openNCA") # 11/11
# pkgload::load_all(path = "codebase/openNCA-a06c1c7461/openNCA") # 11/12
pkgload::load_all(path = "codebase/openNCA-e3be44d927/openNCA") # 11/13
#### Clean test environment ---------------------------------------------- ####
# Clean environment
rm(params, conc, vlist, results, results_list, r, e, mct, d, flags, p, klist, n,
ninc, nexc, k, rlist, dlist, nlist, slist, df, xmin, xmax, dk, flag_data,
est_data, outputdir)
profileid <- "SDEID"
args = commandArgs(trailingOnly=TRUE)
params = c()
#### Test Case 012 ------------------------------------------------------- ####
# M1 SS
# p <- "C:/Users/tensfeldt/Box Sync/EQuIP Computation Engine/Rudraya_executed_test_cases/tc012"
testcase.id <- "tc012"
p <- "testcase/tc012_M1SS"
params["INPUT1"] = file.path(p, "A0081216.in.csv")
params["parameterset"] ="PARAMETERLIST"
params["map"] = file.path(p, "tc012_mct_updated.csv")
params["flags"] = file.path(p, "A0081216_flag_data.in.csv")
params["OUTPUT"] ="."
print(params)
mct <- read_csv(params["map"])
write_file(toJSON(mct, na="string"), path=file.path(p, "tc012_mct_updated.json"))
params["map"] = file.path(p, "tc012_mct_updated.json")
flags <- read_csv(params["flags"])
write_file(toJSON(flags, na="string"), path=file.path(p, "tc012_flags.json"))
params["flags"] = file.path(p, "tc012_flags.json")
#### Read full concentration dataset ------------------------------------- ####
# Read conc
conc <- read.csv(file=params["INPUT1"], header=TRUE, sep=",", as.is=TRUE)
vlist <- c("PKDATAROWID", "PKPTMS", "PKCNCN", "PERIOD", "TREATXT")
# Read mct
mct <- params["map"]
###mct <- read_file(mct)
###mct <- gsub("[\\]+", "", mct)
mct <- fromJSON(mct)
# Read flags
flags <- params["flags"]
###flags <- read_file(flags)
###flags <- gsub("[\\]+", "", flags)
flags <- fromJSON(flags)
# ParameterSet
if(is.na(params["parameterset"])) { params["parameterset"] <- "PARAMETERDISPLAYLIST" }
parameterset <- params["parameterset"]
# FLAGS is a subset for the specific profile, subset concentration dataframe
# to those records by PKDATAROWID
d <- conc[is.element(conc$PKDATAROWID, flags$PKDATAROWID),]
# Add in missing required parameters for virtual compute call
klist <- c("KEL", "KELRSQ", "KELNOPT", "THALFF")
if(!is.element("PARAMETERDISPLAYLIST", names(mct))) { mct$PARAMETERDISPLAYLIST <- "" }
for(i in klist) {
###if(regexpr(i, mct$PARAMETERDISPLAYLIST, ignore.case=TRUE)==-1) { mct$PARAMETERDISPLAYLIST <- c(mct$PARAMETERDISPLAYLIST, i) }
if(regexpr(i, mct$PARAMETERDISPLAYLIST, ignore.case=TRUE)==-1) { mct$PARAMETERDISPLAYLIST <- paste(mct$PARAMETERDISPLAYLIST, i, sep=";") }
}
# Remove any leading semi-colons ";"
mct$PARAMETERDISPLAYLIST <- gsub("^;*", "", mct$PARAMETERDISPLAYLIST, ignore.case=TRUE, perl=TRUE)
# For virtual compute call only: Subset the parameters to be calculated to those in parameterdisplaylist
if(!is.element("PARAMETERLIST", names(mct))) { mct$PARAMETERLIST <- "" }
# Check whether TIME selection has been set in MCT - default to NOMINAL
if(!is.element("TIME", names(mct))) { mct$TIME <- "NOMINAL" }
# Determine whether there are any points selected for inclusion in the terminal elimination phase via
# FLGEXKEL (0=not excluded/included; 1=excluded/not included)
# ***What happens if there are no flags provided?***
n <- factor(flags$FLGEXKEL, levels=c(0,1))
ninc <- length(n[n==0])
nexc <- length(n[n==1])
#### Final changes to test ----------------------------------------------- ####
### Optimise Kel
# mct$OPTIMIZEKEL <- 0 # don't optimise
mct$OPTIMIZEKEL <- 1 # optimise
#### Run computation engine ---------------------------------------------- ####
### Run computation
results_list <- run_computation(data = d, map = mct, flag = flags,
parameterset = parameterset)
### Save results to objects
r <- results_list$data_out
e <- results_list$est_data
f <- results_list$flag_data
### Difference in KEL
head(r[c("SDEID", "KEL", "KELTMLO", "KELTMHI", "KELNOPT", "KELRSQ", "AUCXPCTP", "FLGACCEPTKEL")])
Partial AURCs were specified as follows:
AUC.1.T1 AUC.1.T2 AUC.2.T1 AUC.2.T2 AUC.3.T1 AUC.3.T2
1 2 18 8 16 12 30
The run_computation execution ends in a crash:
> results_list <- run_computation(data=d, map=mct, flag=flags, parameterset=parameterset)
Error in value[[3L]](cond) :
Error in doTryCatch(return(expr), name, parentenv, handler): object 'aurc_par_len' not found
For SDEID 58098661
In addition: Warning message:
In run_M4_SD_computation(data = merged_data, map = map_data, method = method, :
Show Traceback
Rerun with Debug
Error in value[[3L]](cond) :
Error in doTryCatch(return(expr), name, parentenv, handler): object 'aurc_par_len' not found
For SDEID 58098661
Behavior in this case is that the computation engine should report the issue, report what records are impacted by it and generate missing values for all of the expected parameters for the affected PROFILE but not crash and must continue to process and generate parameters for all subsequent profiles.
The following table shows the duplicated TIME records/values for 3 SDEIDs:
PKDATAROWID SDEID SUBJID PKPTMS PKATPD TREATXT PERIOD PKCNCN
170 27 908214575 10011002 0.0 0.000 10 mg 3 0.0000
171 28 908214575 10011002 0.5 0.600 10 mg 3 6.2900
172 29 908214575 10011002 1.0 1.000 10 mg 3 7.9300
173 30 908214575 10011002 1.5 1.500 10 mg 3 7.6500
174 31 908214575 10011002 2.0 2.000 10 mg 3 8.0100
175 32 908214575 10011002 2.5 2.500 10 mg 3 16.8000
176 33 908214575 10011002 4.0 4.000 10 mg 3 23.1000
177 34 908214575 10011002 8.0 8.000 10 mg 3 16.4000
178 35 908214575 10011002 12.0 12.000 10 mg 3 10.1000
179 36 908214575 10011002 16.0 16.000 10 mg 3 7.5800
1791 37 908214575 10011002 16.0 16.000 10 mg 3 15.1600
180 38 908214575 10011002 24.0 24.000 10 mg 3 6.2800
181 39 908214575 10011002 48.0 48.000 10 mg 3 1.7900
182 40 908214575 10011002 72.0 72.000 10 mg 3 0.5880
118 119 257914734 10011005 0.0 0.000 3 mg 2 0.0000
119 120 257914734 10011005 0.5 0.500 3 mg 2 1.7400
120 121 257914734 10011005 1.0 1.000 3 mg 2 2.9900
121 122 257914734 10011005 1.5 1.500 3 mg 2 2.1500
122 123 257914734 10011005 2.0 2.000 3 mg 2 1.8500
123 124 257914734 10011005 2.5 2.500 3 mg 2 1.5200
124 125 257914734 10011005 4.0 4.017 3 mg 2 2.6700
125 126 257914734 10011005 8.0 8.000 3 mg 2 2.8700
126 127 257914734 10011005 12.0 12.017 3 mg 2 1.7400
127 128 257914734 10011005 16.0 16.000 3 mg 2 1.2300
1271 129 257914734 10011005 16.0 16.000 3 mg 2 2.4600
128 130 257914734 10011005 24.0 24.000 3 mg 2 1.2000
129 131 257914734 10011005 48.0 48.000 3 mg 2 0.6720
130 132 257914734 10011005 72.0 72.000 3 mg 2 0.2240
144 198 949633293 10011008 0.0 0.000 3 mg 2 0.0000
145 199 949633293 10011008 0.5 0.500 3 mg 2 1.3700
146 200 949633293 10011008 1.0 1.000 3 mg 2 1.7500
147 201 949633293 10011008 1.5 1.500 3 mg 2 1.2000
148 202 949633293 10011008 2.0 2.000 3 mg 2 1.2400
149 203 949633293 10011008 2.5 2.533 3 mg 2 1.9400
150 204 949633293 10011008 4.0 4.000 3 mg 2 3.5700
151 205 949633293 10011008 8.0 8.000 3 mg 2 3.6500
152 206 949633293 10011008 12.0 12.000 3 mg 2 2.2300
153 207 949633293 10011008 16.0 16.000 3 mg 2 1.3600
1531 208 949633293 10011008 16.0 16.000 3 mg 2 2.7200
154 209 949633293 10011008 24.0 24.000 3 mg 2 1.1800
155 210 949633293 10011008 48.0 48.000 3 mg 2 0.3400
156 211 949633293 10011008 72.0 72.000 3 mg 2 0.0748
and the error from the computation engine crash:
> results_list <- run_computation(data=d, map=mct, flag=flags, parameterset=parameterset)
Error in value[[3L]](cond) :
Error in FUN(X[[i]], ...): Error in kel: unable to calcualte due to duplicate entires in 'time' vector
For SDEID 257914734
In addition: Warning messages:
1: In run_M1_SD_computation(data = merged_data, map = map_data, method = method, :
Flag 'FLGACCEPTTAU' cannot be computed if 'TAUi' is not provided
2: In run_M1_SD_computation(data = merged_data, map = map_data, method = method, :
Show Traceback
Rerun with Debug
Error in value[[3L]](cond) :
Error in FUN(X[[i]], ...): Error in kel: unable to calcualte due to duplicate entires in 'time' vector
For SDEID 257914734
When including multiple partial AUC that extrapolate from the same point. For example for tc015 the partial AUCs 18-505 and 18-550:
This results in the same AUC.
SDEID AUC0_120 AUC80_505 AUC18_505 AUC18_550 AUC0_720 AUC0_505
1 106469843 159513.0 58235.45 144957.0 144957.0 204068.1 204068.1
2 119434169 129250.5 54387.23 122510.6 122510.6 171431.4 171431.4
3 12659123 1158678.4 455358.79 1047471.1 1047471.1 1527374.6 1527374.6
4 133443675 670111.1 224537.42 550947.2 550947.2 846810.7 846810.7
5 147147665 2707675.8 1533806.64 2967803.1 2967803.1 3985682.1 3985682.1
6 162298431 381595.8 110243.82 302533.7 302533.7 464524.6 464524.6
This same behaviour occurs when the point shared by the partial AUCs is zero, and does not affect interpolated values (120).
This has been replicated in tc011, tc012 and tc015. This does not occur in tc010. I am yet to test this for tc013, tc014 and tc016, but they may also be affected.
Following occurs when executing tc012 but warnings from tc011 are similar:
> results_list <- run_computation(data=d, map=mct, flag=flags, parameterset=parameterset)
unit_conversion : Unit Class 1 (Time) time_col: TMAX1 TMAX2 TMIN1 TMIN2 TLAST TLAG KELTMLO KELTMHI THALF LASTTIME MRTEVIFO1 MRTEVIFO2 MRTEVIFP1 MRTEVIFP2 TAU1 TAU2 parameters are scaled from HR to HR via scaling factor: 1
unit_conversion : Unit Class 3 (Dose) dose_col: DOSE1 parameters are scaled from MG to MG via scaling factor: 1
unit_conversion : Unit Class 3 (Dose) dose_col: DOSE2 parameters are scaled from MG to MG via scaling factor: 1
unit_conversion : Unit Class 4 (Volume) volume_col: VZFTAU1 VZFTAU2 parameters are scaled from ML to L via scaling factor: 0.001
unit_conversion : Unit Class 5 (Amount/Volume) conc_col: CMAX1 CMAX2 CMIN1 CMIN2 CLAST1 CLAST2 CTROUGH1 CTROUGH2 CTROUGHEND1 CTROUGHEND2 CAV1 CAV2 parameters are scaled from UG/ML to NG/ML via scaling factor: 1000
unit_conversion : Unit Class 6 (1/Time) kel_col: KEL parameters are scaled from 1/HR to 1/HR via scaling factor: 1
unit_conversion : Unit Class 7 (Volume/Time) cl_col: CLFTAU1 CLFTAU2 parameters are scaled from ML/HR to L/HR via scaling factor: 0.001
unit_conversion : Unit Class 8: (Amount.Time/Volume) auc_col: AUCALL AUCLAST AUCLAST1 AUCLAST2 AUCLASTC1 AUCLASTC2 AUCINFO AUCINFOC AUCINFP AUCINFPC AUCTAU1 AUCTAU2 parameters are scaled from UG.HR/ML to NG.HR/ML via scaling factor: 1000
unit_conversion : Unit Class 9: (Amount.Time.Time/Volume) aumc_col: AUMCTAU1 AUMCTAU2 parameters are scaled from UG.HR.HR/ML to NG.HR.HR/ML via scaling factor: 1000
unit_conversion : Unit Class 12: ([Amount/Volume]/Amount) concdn_col: CMAXDN1 CMAXDN2 parameters are scaled from UG/ML/MG to NG/ML/MG via scaling factor: 1000
There were 50 or more warnings (use warnings() to see the first 50)
> warnings()
Warning messages:
1: In run_M1_SS_computation(data = merged_data, map = map_data, ... :
Flag 'FLGEMESIS' is not present in the dataset
2: In unit_conversion(data = data_data, map = map_data, result = data_data, ... :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
3: In unit_conversion(data = data_data, map = map_data, result = data_data, ... :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
4: In unit_conversion(data = data_data, map = map_data, result = data_data, ... :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
5: In unit_conversion(data = data_data, map = map_data, result = data_data, ... :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
6: In unit_conversion(data = data_data, map = map_data, result = data_data, ... :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
7: In unit_conversion(data = data_data, map = map_data, result = data_data, ... :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
8: In unit_conversion(data = data_data, map = map_data, result = data_data, ... :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
9: In unit_conversion(data = data_data, map = map_data, result = data_data, ... :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
10: In unit_conversion(data = data_data, map = map_data, result = data_data, ... :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
11: In unit_conversion(data = data_data, map = map_data, result = data_data, ... :
@Rudraya, Currently, MCT includes PARAMETERDISPLAYLIST which, out of necessity is required to include the parameters to return for a virtual compute call (i.e. parameterset argument == "PARAMETERDISPLAYLIST") AS WELL AS any data items from the input dataset needed to display in the KEL parameter results pane in the Application Analysis module.
In order to separate the DATA items from the computation engine PARAMETER result items being displayed in the Application Analysis Module a new MCT entry is being introduced with openNCA 2.0.1, the DATADISPLAYLIST.
DATADISPLAYLIST will contain data field names from the input dataset required to display in the KEL parameter results pane in the application and now the PARAMETERDISPLAYLIST will only have names of the parameters required.
It is considered that the virtual compute call wrapper script can be updated to manage changes in openNCA 2.0.1 that are required for this without modification to the compute engine code itself but this issue is entered for awareness and to consider the implications in more depth as development proceeds with openNCA CE 2.0.1. If modifications are required to the wrapper script that calls the compute engine for virtual compute calls, these will be done separately from the work being pursued by @Rudraya .
Partial AUC0-540 was requested but the extrapolation was not done. See image for details.
No est_data output is included in the output for tc016 (unchanged).
This issue was inherited from the master branch.
Running run_computation and checking the values stored for est_data provides the following output:
[1] PKDATAROWID SDEID TIME CEST_KEL CEST_INT CEST_EXT CEST_C0 CEST_TLAST
<0 rows> (or 0-length row.names)
When running tc013 with no changes the following error and traceback is given:
Error in value[[3L]](cond) :
Error in value[[jvseq[[jjj]]]]: subscript out of bounds
For SDEID 10589286
7. stop(paste0(e, "For SDEID ", unique(data_data[, map_data$SDEID])[i])) at run_M2_SD_computation.R#1683
6. value[[3L]](cond)
5. tryCatchOne(expr, names, parentenv, handlers[[1L]])
4. tryCatchList(expr, classes, parentenv, handlers)
3. tryCatch({
tmp_df <- data_data[data_data[, map_data$SDEID] == unique(data_data[,
map_data$SDEID])[i], ]
tmp_df <- tmp_df[order(tmp_df[, map_data$TIME]), ] ... at run_M2_SD_computation.R#804
2. run_M2_SD_computation(data = merged_data, map = map_data, method = method,
model_regex = model_regex, parameter_list = parameter_list,
return_list = return_list) at run_computation.R#2229
1. run_computation(data = d, map = mct, flag = flags, parameterset = parameterset)
This error has been introduced in 6284702 and did not occur in the previous commit e3be44d.
#### Load Libraries ------------------------------------------------------ ####
library(readr)
library(jsonlite)
library(testthat)
#### Load specific code-base for OpenNCA --------------------------------- ####
# pkgload::load_all(path = "codebase/openNCA-master/openNCA")
# pkgload::load_all(path = "codebase/openNCA-a06c1c7461/openNCA") # 11/12
# pkgload::load_all(path = "codebase/openNCA-e3be44d927/openNCA") # 11/13
pkgload::load_all(path = "codebase/openNCA-6284702d62/openNCA") # 11/14
#### Clean test environment ---------------------------------------------- ####
# Clean environment
rm(params, conc, vlist, results, results_list, r, e, mct, d, flags, p, klist, n,
ninc, nexc, k, rlist, dlist, nlist, slist, df, xmin, xmax, dk, flag_data,
est_data, outputdir)
profileid <- "SDEID"
args = commandArgs(trailingOnly=TRUE)
params = c()
#### Test Case 013 ------------------------------------------------------- ####
# M2 SD
# p <- "C:/Users/tensfeldt/Box Sync/EQuIP Computation Engine/Rudraya_executed_test_cases/tc013"
testcase.id <- "tc013"
p <- "testcase/tc013_M2SD"
params["INPUT1"] = file.path(p, "B2901001.in.csv")
params["parameterset"] ="PARAMETERLIST"
params["map"] = file.path(p, "tc013_mct_updated.csv")
params["flags"] = file.path(p, "B2901001_flag_data.in.csv")
params["OUTPUT"] ="."
print(params)
mct <- read_csv(params["map"])
write_file(toJSON(mct, na="string"), path=file.path(p, "tc013_mct_updated.json"))
params["map"] = file.path(p, "tc013_mct_updated.json")
flags <- read_csv(params["flags"])
write_file(toJSON(flags, na="string"), path=file.path(p, "tc013_flags.json"))
params["flags"] = file.path(p, "tc013_flags.json")
#### Read full concentration dataset ------------------------------------- ####
# Read conc
conc <- read.csv(file=params["INPUT1"], header=TRUE, sep=",", as.is=TRUE)
vlist <- c("PKDATAROWID", "PKPTMS", "PKCNCN", "PERIOD", "TREATXT")
# Read mct
mct <- params["map"]
###mct <- read_file(mct)
###mct <- gsub("[\\]+", "", mct)
mct <- fromJSON(mct)
# Read flags
flags <- params["flags"]
###flags <- read_file(flags)
###flags <- gsub("[\\]+", "", flags)
flags <- fromJSON(flags)
# ParameterSet
if(is.na(params["parameterset"])) { params["parameterset"] <- "PARAMETERDISPLAYLIST" }
parameterset <- params["parameterset"]
# FLAGS is a subset for the specific profile, subset concentration dataframe
# to those records by PKDATAROWID
d <- conc[is.element(conc$PKDATAROWID, flags$PKDATAROWID),]
# Add in missing required parameters for virtual compute call
klist <- c("KEL", "KELRSQ", "KELNOPT", "THALFF")
if(!is.element("PARAMETERDISPLAYLIST", names(mct))) { mct$PARAMETERDISPLAYLIST <- "" }
for(i in klist) {
###if(regexpr(i, mct$PARAMETERDISPLAYLIST, ignore.case=TRUE)==-1) { mct$PARAMETERDISPLAYLIST <- c(mct$PARAMETERDISPLAYLIST, i) }
if(regexpr(i, mct$PARAMETERDISPLAYLIST, ignore.case=TRUE)==-1) { mct$PARAMETERDISPLAYLIST <- paste(mct$PARAMETERDISPLAYLIST, i, sep=";") }
}
# Remove any leading semi-colons ";"
mct$PARAMETERDISPLAYLIST <- gsub("^;*", "", mct$PARAMETERDISPLAYLIST, ignore.case=TRUE, perl=TRUE)
# For virtual compute call only: Subset the parameters to be calculated to those in parameterdisplaylist
if(!is.element("PARAMETERLIST", names(mct))) { mct$PARAMETERLIST <- "" }
# Check whether TIME selection has been set in MCT - default to NOMINAL
if(!is.element("TIME", names(mct))) { mct$TIME <- "NOMINAL" }
# Determine whether there are any points selected for inclusion in the terminal elimination phase via
# FLGEXKEL (0=not excluded/included; 1=excluded/not included)
# ***What happens if there are no flags provided?***
n <- factor(flags$FLGEXKEL, levels=c(0,1))
ninc <- length(n[n==0])
nexc <- length(n[n==1])
#### Run computation engine ---------------------------------------------- ####
### Run computation
# pretime <- Sys.time()
results_list <- run_computation(data = d, map = mct, flag = flags,
parameterset = parameterset)
When setting INCLUDEINTERPOLATION in the mct to 0, interpolation still occurs. This has been tested and found to occur for tc010, tc011, tc012, tc013, tc014 (using historic commit 066fa5d), tc015, tc016. Testing of test cases without interpolation requested in the mct were changed to request a partial AUC with interpolation.
mct$INCLUDEINTERPOLATION <- 1
results_list <- run_computation(data = d, map = mct, flag = flags,
parameterset = parameterset)
r_on <- results_list$data_out
e_on <- results_list$est_data
f_on <- results_list$flag_data
mct$INCLUDEINTERPOLATION <- 0
results_list <- run_computation(data = d, map = mct, flag = flags,
parameterset = parameterset)
r_off <- results_list$data_out
e_off <- results_list$est_data
f_off <- results_list$flag_data
all.equal(r_on, r_off)
all.equal(e_on, e_off)
all.equal(f_on, f_off)
> all.equal(r_on, r_off)
[1] TRUE
> all.equal(e_on, e_off)
[1] TRUE
> all.equal(f_on, f_off)
[1] TRUE
Behavior in this case is that the computation engine should report the issue, report what records are impacted by it and ignore the duplication, i.e. effectively ignore one of the records with the duplicated concentration and time values but continue to generate parameters.
The following table shows the duplicated values for 3 SDEIDs:
PKDATAROWID SDEID SUBJID PKPTMS PKATPD TREATXT PERIOD PKCNCN
170 27 908214575 10011002 0.0 0.000 10 mg 3 0.0000
171 28 908214575 10011002 0.5 0.600 10 mg 3 6.2900
172 29 908214575 10011002 1.0 1.000 10 mg 3 7.9300
173 30 908214575 10011002 1.5 1.500 10 mg 3 7.6500
174 31 908214575 10011002 2.0 2.000 10 mg 3 8.0100
175 32 908214575 10011002 2.5 2.500 10 mg 3 16.8000
176 33 908214575 10011002 4.0 4.000 10 mg 3 23.1000
177 34 908214575 10011002 8.0 8.000 10 mg 3 16.4000
178 35 908214575 10011002 12.0 12.000 10 mg 3 10.1000
179 36 908214575 10011002 16.0 16.000 10 mg 3 7.5800
1791 37 908214575 10011002 16.0 16.000 10 mg 3 7.5800
180 38 908214575 10011002 24.0 24.000 10 mg 3 6.2800
181 39 908214575 10011002 48.0 48.000 10 mg 3 1.7900
182 40 908214575 10011002 72.0 72.000 10 mg 3 0.5880
118 119 257914734 10011005 0.0 0.000 3 mg 2 0.0000
119 120 257914734 10011005 0.5 0.500 3 mg 2 1.7400
120 121 257914734 10011005 1.0 1.000 3 mg 2 2.9900
121 122 257914734 10011005 1.5 1.500 3 mg 2 2.1500
122 123 257914734 10011005 2.0 2.000 3 mg 2 1.8500
123 124 257914734 10011005 2.5 2.500 3 mg 2 1.5200
124 125 257914734 10011005 4.0 4.017 3 mg 2 2.6700
125 126 257914734 10011005 8.0 8.000 3 mg 2 2.8700
126 127 257914734 10011005 12.0 12.017 3 mg 2 1.7400
127 128 257914734 10011005 16.0 16.000 3 mg 2 1.2300
1271 129 257914734 10011005 16.0 16.000 3 mg 2 1.2300
128 130 257914734 10011005 24.0 24.000 3 mg 2 1.2000
129 131 257914734 10011005 48.0 48.000 3 mg 2 0.6720
130 132 257914734 10011005 72.0 72.000 3 mg 2 0.2240
144 198 949633293 10011008 0.0 0.000 3 mg 2 0.0000
145 199 949633293 10011008 0.5 0.500 3 mg 2 1.3700
146 200 949633293 10011008 1.0 1.000 3 mg 2 1.7500
147 201 949633293 10011008 1.5 1.500 3 mg 2 1.2000
148 202 949633293 10011008 2.0 2.000 3 mg 2 1.2400
149 203 949633293 10011008 2.5 2.533 3 mg 2 1.9400
150 204 949633293 10011008 4.0 4.000 3 mg 2 3.5700
151 205 949633293 10011008 8.0 8.000 3 mg 2 3.6500
152 206 949633293 10011008 12.0 12.000 3 mg 2 2.2300
153 207 949633293 10011008 16.0 16.000 3 mg 2 1.3600
1531 208 949633293 10011008 16.0 16.000 3 mg 2 1.3600
154 209 949633293 10011008 24.0 24.000 3 mg 2 1.1800
155 210 949633293 10011008 48.0 48.000 3 mg 2 0.3400
156 211 949633293 10011008 72.0 72.000 3 mg 2 0.0748
and the error from the computation engine crash:
> results_list <- run_computation(data=d, map=mct, flag=flags, parameterset=parameterset)
Error in value[[3L]](cond) :
Error in FUN(X[[i]], ...): Error in kel: unable to calcualte due to duplicate entires in 'time' vector
For SDEID 257914734
In addition: Warning messages:
1: In run_M1_SD_computation(data = merged_data, map = map_data, method = method, :
Flag 'FLGACCEPTTAU' cannot be computed if 'TAUi' is not provided
2: In run_M1_SD_computation(data = merged_data, map = map_data, method = method, :
Show Traceback
Rerun with Debug
Error in value[[3L]](cond) :
Error in FUN(X[[i]], ...): Error in kel: unable to calcualte due to duplicate entires in 'time' vector
For SDEID 257914734
Partial AURCs requested are:
AUC.1.T1 AUC.1.T2
1 0 24
Partial printout of parameters generated indicate that no Partial AURCs are generated:
SDEID AMT.12.00 AMT.24.00 AE.12.00 AE.24.00 AEPCT.12.00 AEPCT.24.00 AE AEPCT MAXRATE1 TMAXRATE1 RATELAST1 MIDPTLAST1 TLAG KEL KELTMLO KELTMHI KELNOPT KELR KELRSQ
1 102652097 79.3760 27.7380 79.3760 107.1140 39688000 53557000 107.1140 53557000 6614.6667 6 2311.5000 18 NA NA NA NA 0 NA NA
2 155315252 41.3270 22.2250 41.3270 63.5520 20663500 31776000 63.5520 31776000 3443.9167 6 1852.0833 18 NA NA NA NA 0 NA NA
3 212128320 66.4200 35.6890 66.4200 102.1090 33210000 51054500 102.1090 51054500 5535.0000 6 2974.0833 18 NA NA NA NA 0 NA NA
4 232007206 43.4750 34.0375 43.4750 77.5125 43475000 77512500 77.5125 77512500 3622.9167 6 3622.9167 6 NA NA NA NA 0 NA NA
5 303069272 4.3056 3.0774 4.3056 7.3830 14352000 24610000 7.3830 24610000 358.8000 6 256.4500 18 NA NA NA NA 0 NA NA
6 322184155 44.7300 4.6200 44.7300 49.3500 22365000 24675000 49.3500 24675000 3727.5000 6 385.0000 18 NA NA NA NA 0 NA NA
7 346821548 8.5995 1.8480 8.5995 10.4475 28665000 34825000 10.4475 34825000 716.6250 6 154.0000 18 NA NA NA NA 0 NA NA
8 359622627 43.5400 34.5466 43.5400 78.0866 21770000 39043300 78.0866 39043300 3628.3333 6 2878.8833 18 NA NA NA NA 0 NA NA
9 392430764 35.1200 28.1775 35.1200 63.2975 35120000 63297500 63.2975 63297500 2926.6667 6 2926.6667 6 NA NA NA NA 0 NA NA
10 395827329 62.4800 28.0416 62.4800 90.5216 31240000 45260800 90.5216 45260800 5206.6667 6 2336.8000 18 NA NA NA NA 0 NA NA
11 43456658 6.3280 3.7180 6.3280 10.0460 21093333 33486667 10.0460 33486667 527.3333 6 309.8333 18 NA NA NA NA 0 NA NA
KELRSQA FLGACCEPTKEL THALF THALFF AURCALL AURCLAST AURCINFO AURCINFP AURCXPCTO AURCXPCTP VOLSUM RATE1 RATE2 MIDPT1 MIDPT2 DI1 TAU1 TOLD1 DOSE1 TIMEU AMOUNTU DOSEU VOLUMEU CONCU
1 NA 0 NA <NA> NA NA NA NA NA NA 3.760 NA NA 6 18 NA 24 0 200 HR MG MG L NG/ML
2 NA 0 NA <NA> NA NA NA NA NA NA 3.085 NA NA 6 18 NA 24 0 200 HR MG MG L NG/ML
3 NA 0 NA <NA> NA NA NA NA NA NA 2.940 NA NA 6 18 NA 24 0 200 HR MG MG L NG/ML
4 NA 0 NA <NA> NA NA NA NA NA NA 2.725 NA NA 6 18 NA 12 0 100 HR MG MG L NG/ML
5 NA 0 NA <NA> NA NA NA NA NA NA 2.160 NA NA 6 18 NA 24 0 30 HR MG MG L NG/ML
6 NA 0 NA <NA> NA NA NA NA NA NA 2.083 NA NA 6 18 NA 24 0 200 HR MG MG L NG/ML
7 NA 0 NA <NA> NA NA NA NA NA NA 1.190 NA NA 6 18 NA 24 0 30 HR MG MG L NG/ML
8 NA 0 NA <NA> NA NA NA NA NA NA 2.166 NA NA 6 18 NA 24 0 200 HR MG MG L NG/ML
9 NA 0 NA <NA> NA NA NA NA NA NA 1.775 NA NA 6 18 NA 12 0 100 HR MG MG L NG/ML
10 NA 0 NA <NA> NA NA NA NA NA NA 1.608 NA NA 6 18 NA 24 0 200 HR MG MG L NG/ML
11 NA 0 NA <NA> NA NA NA NA NA NA 1.210 NA NA 6 18 NA 24 0 30 HR MG MG L NG/ML
KELU CLU AUCU AUMCU AUCNORMU AURCU CONCNORMU RATEU VOLUMEWU CLWU STUDY SITEID SUBJID RAND TREATXT TRTCD PKCOLL PKBDFLD PKTERM PERIODU
1 1/HR NA NA NA NA ML.UG/ML NA UG/HR NA NA B1731007 1001 10011198 1056 PF-05175157- 200mg - T2DM cohort F INTERVAL URINE PF-05175157 NA
2 1/HR NA NA NA NA ML.UG/ML NA UG/HR NA NA B1731007 1001 10011132 1053 PF-05175157- 200mg - T2DM cohort F INTERVAL URINE PF-05175157 NA
3 1/HR NA NA NA NA ML.UG/ML NA UG/HR NA NA B1731007 1001 10011219 1062 PF-05175157- 200mg - T2DM cohort F INTERVAL URINE PF-05175157 NA
4 1/HR NA NA NA NA ML.UG/ML NA UG/HR NA NA B1731007 1001 10011130 1042 PF-05175157- 100 mg BID- HOV Cohort E INTERVAL URINE PF-05175157 NA
5 1/HR NA NA NA NA ML.UG/ML NA UG/HR NA NA B1731007 1001 10011007 1006 PF-05175157- 30mg - HOV Cohort B INTERVAL URINE PF-05175157 NA
6 1/HR NA NA NA NA ML.UG/ML NA UG/HR NA NA B1731007 1001 10011048 1026 PF-05175157- 200mg - HOV Cohort D INTERVAL URINE PF-05175157 NA
7 1/HR NA NA NA NA ML.UG/ML NA UG/HR NA NA B1731007 1001 10011023 1011 PF-05175157- 30mg - HOV Cohort B INTERVAL URINE PF-05175157 NA
8 1/HR NA NA NA NA ML.UG/ML NA UG/HR NA NA B1731007 1001 10011078 1033 PF-05175157- 200mg - HOV Cohort D INTERVAL URINE PF-05175157 NA
9 1/HR NA NA NA NA ML.UG/ML NA UG/HR NA NA B1731007 1001 10011128 1040 PF-05175157- 100 mg BID- HOV Cohort E INTERVAL URINE PF-05175157 NA
10 1/HR NA NA NA NA ML.UG/ML NA UG/HR NA NA B1731007 1001 10011086 1036 PF-05175157- 200mg - HOV Cohort D INTERVAL URINE PF-05175157 NA
11 1/HR NA NA NA NA ML.UG/ML NA UG/HR NA NA B1731007 1001 10011011 1008 PF-05175157- 30mg - HOV Cohort B INTERVAL URINE PF-05175157 NA
PERIOD VISITU VISIT PHASE HT WT AGEDERU AGEDER WTUNI WTRAW HTUNI HTRAW RACEOTH RACES SEX RACIALD ETHNIC ACTTRT ACTTRTS ACTTRTC PKPCOM
1 NA DAY 14 NA 168.91 NA YR 54 NA NA IN 66.5 NA W M NA NA PF-05175157- 200mg - T2DM cohort F NA <NA>
2 NA DAY 14 NA 167.89 NA YR 43 NA NA IN 66.1 NA W M NA NA PF-05175157- 200mg - T2DM cohort F NA <NA>
3 NA DAY 14 NA 169.93 NA YR 48 NA NA IN 66.9 NA W M NA NA PF-05175157- 200mg - T2DM cohort F NA <NA>
4 NA DAY 14 NA 174.50 NA YR 41 NA NA IN 68.7 NA W M NA NA PF-05175157- 100 mg BID- HOV Cohort E NA <NA>
5 NA DAY 14 NA 175.26 NA YR 22 NA NA IN 69.0 NA W M NA NA PF-05175157- 30mg - HOV Cohort B NA <NA>
6 NA DAY 14 NA 170.94 NA YR 32 NA NA IN 67.3 NA W M NA NA PF-05175157- 200mg - HOV Cohort D NA <NA>
7 NA DAY 14 NA 175.77 NA YR 40 NA NA IN 69.2 NA W M NA NA PF-05175157- 30mg - HOV Cohort B NA <NA>
8 NA DAY 14 NA 162.56 NA YR 43 NA NA IN 64.0 NA W M NA NA PF-05175157- 200mg - HOV Cohort D NA <NA>
9 NA DAY 14 NA 175.77 NA YR 31 NA NA IN 69.2 NA W M NA NA PF-05175157- 100 mg BID- HOV Cohort E NA <NA>
10 NA DAY 14 NA 160.78 NA YR 34 NA NA IN 63.3 NA W M NA NA PF-05175157- 200mg - HOV Cohort D NA <NA>
11 NA DAY 14 NA 170.94 NA YR 33 NA NA IN 67.3 NA W M NA NA PF-05175157- 30mg - HOV Cohort B NA <NA>
When setting INCLUDEEXTRAPOLATION in the mct to 0, extrapolation still occurs in tc010. This issue appears to be specific to M1SD as INCLUDEEXTRAPOLATION turns off extrapolation for tc011, tc012, tc013, tc014 (using historic commit 066fa5d), tc015 and tc016.
The output for partial AUCs for these testcases where INCLUDEEXTRAPOLATION "works" but it isn't clear to me if they work as desired.
mct$INCLUDEEXTRAPOLATION <- 1
results_list <- run_computation(data = d, map = mct, flag = flags,
parameterset = parameterset)
r_on <- results_list$data_out
e_on <- results_list$est_data
f_on <- results_list$flag_data
mct$INCLUDEEXTRAPOLATION <- 0
results_list <- run_computation(data = d, map = mct, flag = flags,
parameterset = parameterset)
r_off <- results_list$data_out
e_off <- results_list$est_data
f_off <- results_list$flag_data
all.equal(r_on, r_off)
all.equal(e_on, e_off)
all.equal(f_on, f_off)
> all.equal(r_on, r_off)
[1] TRUE
> all.equal(e_on, e_off)
[1] TRUE
> all.equal(f_on, f_off)
[1] TRUE
When testing with tc010 (M1SD) with mct$FLGACCEPTKELCRIT <- "KELRSQ>=0.9,KELNOPT>=3,AUCXPCTP<=20.0"
and comparing runs with no-optimization, i.e. Analyst selected points for terminal elimination phase, against optimization selection, there is at least one profile where the optimization routine doesn't select a data points that are appropriate, i.e. see SDEID == 257914734 below. The "optimized" selection of points results in KELNOPT =10, KELRSQ==0.8988036, AUCXPCTP==10.26398558 and thus FLGACCEPTKEL==0. The analyst chosen points ensures that KELRSQ is > 0.9, i.e. 0.9613906 but with KELNOPT==5 and AUCXPCTP==9.76685944.
It appears that the optimization routine is favoring KELNOPT and AUCXPCTP over KELRSQ but this is not acceptable. Note that none of the runs included FLGNOCMAX included in the mct for optimization runs.
Note that this same behavior is exhibited with tc012. The results of that execution are reported in a separate issue.
SDEID SUBJID.nonopt KEL.nonopt KELRSQ.nonopt KELNOPT.nonopt AUCXPCTP.nonopt FLGACCEPTKEL.nonopt SUBJID.opt KEL.opt KELRSQ.opt KELNOPT.opt AUCXPCTP.opt FLGACCEPTKEL.opt
1 111299859 10011008 0.05566002 0.9988961 3 7.23522456 1 10011008 0.05815238 0.9947858 4 6.81888591 1
2 112146587 10011003 0.08330574 0.9951571 4 1.99934371 1 10011003 0.08695843 0.9906898 5 1.83937140 1
3 123811839 10011011 0.05281662 0.9636440 7 0.08823883 1 10011011 0.06147205 0.9436264 10 0.05578474 1
4 145593586 10011020 0.04739864 0.9966203 4 0.47100278 1 10011020 0.04258080 0.9882692 9 0.58769593 1
5 202367199 10011021 0.04684777 0.9835298 6 0.19312645 1 10011021 0.05424527 0.9623526 9 0.13064667 1
6 233891605 10011016 0.05222238 0.9894347 6 0.09322297 1 10011016 0.05989964 0.9674385 9 0.06301853 1
7 257914734 10011005 0.03131748 0.9613906 5 9.76685944 1 10011005 0.03062752 0.8988036 10 10.26398558 0
8 2925168 10011002 0.04431395 0.9997271 3 4.66602992 1 10011002 0.04240636 0.9980178 5 4.98349076 1
9 355441192 10011014 0.04208899 0.9992629 4 0.33226979 1 10011014 0.04880107 0.9846049 8 0.23672166 1
10 40876220 10011006 0.03813488 0.9655010 4 6.23791345 1 10011006 0.04126708 0.9609512 5 5.54311872 1
11 409149207 10011011 0.05021346 0.9543205 7 0.11182722 1 10011011 0.05697667 0.9365584 9 0.07813746 1
12 427589065 10011023 0.04673851 0.9773159 6 0.16395512 1 10011023 0.05595121 0.9459362 9 0.10094153 1
13 443820693 10011007 0.04421840 0.9823067 6 4.70630987 1 10011007 0.04372414 0.9785370 5 4.79813326 1
14 463304641 10011009 0.03739737 0.9407686 5 5.71496594 1 10011009 0.04165165 0.9198912 6 4.79325626 1
15 471081571 10011001 0.04627941 1.0000000 3 2.99165240 1 10011001 0.04888617 0.9900202 6 2.70670195 1
16 499820574 10011018 0.04165683 0.9703506 6 0.35211658 1 10011018 0.04941609 0.9499996 9 0.23018682 1
17 500091016 10011004 0.05335558 0.9999235 3 2.70851854 1 10011004 0.04493265 0.9655896 10 3.66911987 1
18 500805530 10011013 0.03427675 0.9996774 3 0.67790230 1 10011013 0.04440325 0.9793138 8 0.41060023 1
19 503192693 10011017 0.03532785 0.9998712 4 0.49131046 1 10011017 0.04686789 0.9710978 8 0.27016466 1
20 507682703 10011004 0.06333590 0.9887088 5 1.10940746 1 10011004 0.06333590 0.9887088 5 1.10940746 1
21 518707064 10011015 0.03432966 0.9893330 4 0.43744959 1 10011015 0.05271560 0.9465682 10 0.16557760 1
22 524844737 10011013 0.02935190 0.9976472 3 0.86654332 1 10011013 0.04307692 0.9622531 8 0.42210649 1
23 539272807 10011010 0.04133378 0.9955633 6 0.52463423 1 10011010 0.04518451 0.9808084 8 0.42557031 1
24 565486844 10011026 0.03199563 0.9952792 4 1.01580296 1 10011026 0.04391688 0.9360280 10 0.50664052 1
25 577177821 10011010 0.04579767 0.9918967 6 0.26211759 1 10011010 0.05018806 0.9772797 8 0.20836321 1
26 579271281 10011019 0.03547044 0.9990374 3 0.27534233 1 10011019 0.05372507 0.9648265 8 0.11519217 1
27 582963447 10011006 0.05260636 0.9991340 3 2.70867797 1 10011006 0.05053362 0.9945994 5 2.87814254 1
28 621374373 10011025 0.03378230 0.9809676 4 0.46584953 1 10011025 0.04884008 0.9593891 8 0.21422584 1
29 63536292 10011003 0.08502919 0.9889092 3 14.58611253 1 10011003 0.08502919 0.9889092 3 14.58611253 1
30 652003114 10011022 0.02668476 0.9898088 4 1.85008395 1 10011022 0.03708543 0.9626457 8 1.00976424 1
31 668240634 10011014 0.03609468 0.9940950 4 0.43732511 1 10011014 0.04998195 0.9592045 9 0.21119266 1
32 670348158 10011018 0.02964177 0.9478122 7 2.35042936 1 10011018 0.03355781 0.9507289 8 1.96793076 1
33 732449590 10011026 0.02936040 0.9993806 4 2.41913473 1 10011026 0.03235872 0.9941782 7 2.04753778 1
34 754174517 10011023 0.02756380 0.9961138 3 1.02224944 1 10011023 0.04595063 0.9720685 9 0.40095666 1
35 759109991 10011017 0.03745894 0.9993944 3 0.26760127 1 10011017 0.05460425 0.9633959 9 0.11642010 1
36 773547601 10011007 0.04050294 0.9863159 6 5.92845655 1 10011007 0.03561278 0.9504574 9 7.43474461 1
37 822445112 10011001 0.03273358 0.9955421 4 7.68404767 1 10011001 0.03986173 0.9618308 7 5.66672776 1
38 832770130 10011019 0.03672732 0.9982046 3 0.70887642 1 10011019 0.04341118 0.9959798 10 0.54369069 1
39 836945347 10011015 0.03595652 0.9942042 4 0.32536639 1 10011015 0.05512637 0.9614452 10 0.12251302 1
40 908214575 10011002 0.04934163 0.9988047 3 2.96074170 1 10011002 0.04935120 0.9893417 6 2.91998068 1
41 919470224 10011012 0.03779177 0.9991832 3 0.48016959 1 10011012 0.04762913 0.9812637 8 0.29612886 1
42 921404682 10011016 0.04101774 0.9977553 3 0.20593715 1 10011016 0.05790952 0.9787239 10 0.09482574 1
43 922793085 10011012 0.04543198 0.9889319 7 0.36714559 1 10011012 0.04708142 0.9848534 8 0.33577338 1
44 927491002 10011027 0.04082005 0.9985587 3 0.29914548 1 10011027 0.05079954 0.9910480 8 0.19388510 1
45 935549083 10011009 0.03852380 0.9998668 4 5.42144297 1 10011009 0.04309364 0.9745427 6 4.52221548 1
46 949633293 10011008 0.05746775 0.9968212 3 1.88276503 1 10011008 0.05398483 0.9893433 5 2.07933473 1
47 954855373 10011024 0.04864521 0.9585562 6 0.11235917 1 10011024 0.06186859 0.9429812 11 0.05618289 1
48 978075228 10011005 0.04315283 0.9848178 3 9.84141179 1 10011005 0.05431844 0.9364206 5 7.19276947 1
When running tc014 with no changes the following error and traceback is given:
Error in `[<-.data.frame`(`*tmp*`, eval(parse(text = flag_subset)), , :
missing values are not allowed in subscripted assignments of data frames
5. stop("missing values are not allowed in subscripted assignments of data frames")
4. `[<-.data.frame`(`*tmp*`, eval(parse(text = flag_subset)), ,
value = structure(list(SDEID = c(NA, NA, NA, NA, NA, "551055579",
NA, NA, NA, NA, NA), DOSE1 = c(NA, NA, NA, NA, NA, "1006",
NA, NA, NA, NA, NA), DOSEC = c(NA, NA, NA, NA, NA, "1006", ... at run_M2_SS_computation.R#2331
3. `[<-`(`*tmp*`, eval(parse(text = flag_subset)), , value = structure(list(
SDEID = c(NA, NA, NA, NA, NA, "551055579", NA, NA, NA, NA,
NA), DOSE1 = c(NA, NA, NA, NA, NA, "1006", NA, NA, NA, NA,
NA), DOSEC = c(NA, NA, NA, NA, NA, "1006", NA, NA, NA, NA, ... at run_M2_SS_computation.R#2331
2. run_M2_SS_computation(data = merged_data, map = map_data, method = method,
model_regex = model_regex, parameter_list = parameter_list,
return_list = return_list) at run_computation.R#2235
1. run_computation(data = d, map = mct, flag = flags, parameterset = parameterset)
This error has been introduced in 6284702 and did not occur in the previous commit e3be44d.
#### Load Libraries ------------------------------------------------------ ####
library(readr)
library(jsonlite)
library(testthat)
#### Load specific code-base for OpenNCA --------------------------------- ####
# pkgload::load_all(path = "codebase/openNCA-master/openNCA")
# pkgload::load_all(path = "codebase/openNCA-a06c1c7461/openNCA") # 11/12
# pkgload::load_all(path = "codebase/openNCA-e3be44d927/openNCA") # 11/13
pkgload::load_all(path = "codebase/openNCA-6284702d62/openNCA") # 11/14
#### Clean test environment ---------------------------------------------- ####
# Clean environment
rm(params, conc, vlist, results, results_list, r, e, mct, d, flags, p, klist, n,
ninc, nexc, k, rlist, dlist, nlist, slist, df, xmin, xmax, dk, flag_data,
est_data, outputdir)
profileid <- "SDEID"
args = commandArgs(trailingOnly=TRUE)
params = c()
#### Test Case 014 ------------------------------------------------------- ####
# M2 SS - nominal time
# p <- "C:/Users/tensfeldt/Box Sync/EQuIP Computation Engine/Rudraya_executed_test_cases/tc014"
testcase.id <- "tc014"
p <- "testcase/tc014_M2SS"
params["INPUT1"] = file.path(p, "A4021010.in.csv")
params["parameterset"] ="PARAMETERLIST"
params["map"] = file.path(p, "tc014_mct_updated.csv")
params["flags"] = file.path(p, "A4021010_flag_data.in.csv")
params["OUTPUT"] ="."
print(params)
mct <- read_csv(params["map"])
write_file(toJSON(mct, na="string"), path=file.path(p, "tc014_mct_updated.json"))
params["map"] = file.path(p, "tc014_mct_updated.json")
flags <- read_csv(params["flags"])
write_file(toJSON(flags, na="string"), path=file.path(p, "tc014_flags.json"))
params["flags"] = file.path(p, "tc014_flags.json")
#### Read full concentration dataset ------------------------------------- ####
# Read conc
conc <- read.csv(file=params["INPUT1"], header=TRUE, sep=",", as.is=TRUE)
vlist <- c("PKDATAROWID", "PKPTMS", "PKCNCN", "PERIOD", "TREATXT")
# Read mct
mct <- params["map"]
###mct <- read_file(mct)
###mct <- gsub("[\\]+", "", mct)
mct <- fromJSON(mct)
# Read flags
flags <- params["flags"]
###flags <- read_file(flags)
###flags <- gsub("[\\]+", "", flags)
flags <- fromJSON(flags)
# ParameterSet
if(is.na(params["parameterset"])) { params["parameterset"] <- "PARAMETERDISPLAYLIST" }
parameterset <- params["parameterset"]
# FLAGS is a subset for the specific profile, subset concentration dataframe
# to those records by PKDATAROWID
d <- conc[is.element(conc$PKDATAROWID, flags$PKDATAROWID),]
# Add in missing required parameters for virtual compute call
klist <- c("KEL", "KELRSQ", "KELNOPT", "THALFF")
if(!is.element("PARAMETERDISPLAYLIST", names(mct))) { mct$PARAMETERDISPLAYLIST <- "" }
for(i in klist) {
###if(regexpr(i, mct$PARAMETERDISPLAYLIST, ignore.case=TRUE)==-1) { mct$PARAMETERDISPLAYLIST <- c(mct$PARAMETERDISPLAYLIST, i) }
if(regexpr(i, mct$PARAMETERDISPLAYLIST, ignore.case=TRUE)==-1) { mct$PARAMETERDISPLAYLIST <- paste(mct$PARAMETERDISPLAYLIST, i, sep=";") }
}
# Remove any leading semi-colons ";"
mct$PARAMETERDISPLAYLIST <- gsub("^;*", "", mct$PARAMETERDISPLAYLIST, ignore.case=TRUE, perl=TRUE)
# For virtual compute call only: Subset the parameters to be calculated to those in parameterdisplaylist
if(!is.element("PARAMETERLIST", names(mct))) { mct$PARAMETERLIST <- "" }
# Check whether TIME selection has been set in MCT - default to NOMINAL
if(!is.element("TIME", names(mct))) { mct$TIME <- "NOMINAL" }
# Determine whether there are any points selected for inclusion in the terminal elimination phase via
# FLGEXKEL (0=not excluded/included; 1=excluded/not included)
# ***What happens if there are no flags provided?***
n <- factor(flags$FLGEXKEL, levels=c(0,1))
ninc <- length(n[n==0])
nexc <- length(n[n==1])
#### Run computation engine ---------------------------------------------- ####
### Run computation
# pretime <- Sys.time()
results_list <- run_computation(data = d, map = mct, flag = flags,
parameterset = parameterset)
This WAS working appropriately in current master branch, i.e. expectation is that all parameters other than AUCs are computed as expected. Requested AUCs are set to NA.
flags$FLGEXAUC <- rep(1, nrow(flags)) # exclude all records/data points from KEL calcs
> results_list <- run_computation(data=d, map=mct, flag=flags, parameterset=parameterset)
Error in value[[3L]](cond) : Error in tmp[[2]]: subscript out of bounds
For SDEID 2925168
In addition: Warning messages:
1: In run_M1_SD_computation(data = merged_data, map = map_data, method = method, :
Flag 'FLGACCEPTTAU' cannot be computed if 'TAUi' is not provided
2: In run_M1_SD_computation(data = merged_data, map = map_data, method = method, :
Show Traceback
Rerun with Debug
Error in value[[3L]](cond) : Error in tmp[[2]]: subscript out of bounds
For SDEID 2925168
When using tc015 and using the value of "PARAMETERDISPLAYLIST" for parameterset the computation engine crashes. This does not cause an error for tc010, tc011, tc013 or tc014. Errors for tc012 and tc016 are documented separately due to their unique error messages.
parameterset <- "PARAMETERDISPLAYLIST"
Error in value[[3L]](cond) :
Error in `[<-.data.frame`(`*tmp*`, i, , value = c(12659123, 1e+05, 32700, : replacement has 56 items, need 57
For SDEID 12659123
7. stop(paste0(e, "For SDEID ", unique(data_data[, map_data$SDEID])[i])) at run_M3_SD_computation.R#1659
6. value[[3L]](cond)
5. tryCatchOne(expr, names, parentenv, handlers[[1L]])
4. tryCatchList(expr, classes, parentenv, handlers)
3. tryCatch({
tmp_df <- data_data[data_data[, map_data$SDEID] == unique(data_data[,
map_data$SDEID])[i], ]
tmp_df <- tmp_df[order(tmp_df[, map_data$TIME]), ] ... at run_M3_SD_computation.R#814
2. run_M3_SD_computation(data = merged_data, map = map_data, method = method,
model_regex = model_regex, parameter_list = parameter_list,
return_list = return_list) at run_computation.R#2241
1. run_computation(data = d, map = mct, flag = flags, parameterset = parameterset)
Partial AUCs where both T1 and T2 times are both extrapolated cause crash with subscript out of bounds error.
Note here that the following sampling times are available and the requested partial AUCs are a mixture of partial AUCs with interpolated values and extrapolated values
> table(d$PKPTMS, d$TREATXT)
1 mg 10 mg 100 mg 3 mg 30 mg 300 mg 600 mg 900 mg
0 6 6 6 6 6 6 6 6
0.5 6 6 6 6 6 6 6 6
1 6 6 6 6 6 6 6 6
1.5 6 6 0 6 0 0 0 0
2 6 6 6 6 6 6 6 6
2.5 6 6 0 6 0 0 0 0
4 6 6 6 6 6 6 6 6
8 6 6 6 6 6 6 6 6
12 6 6 6 6 6 6 6 6
16 6 6 6 6 6 6 6 6
24 6 6 6 6 6 6 6 6
48 6 6 6 6 6 6 6 6
72 6 6 6 6 6 6 6 6
96 0 0 6 0 6 6 6 6
120 0 0 6 0 6 6 6 6
Where the partial AUCs are specified through a mix of only interpolated 1 and 3, designed 2 time
points, the parameters generated and estimated concentration results appear rational.
AUC.1.T1 AUC.1.T2 AUC.2.T1 AUC.2.T2 AUC.3.T1 AUC.3.T2
1 0 7 8 48 0 64
PKDATAROWID SDEID TIME CEST_KEL CEST_INT CEST_EXT CEST_C0 CEST_TLAST
4 NA 2925168 7 NA 3.59500000 NA NA NA
1 22289754 2925168 24 1.70263761 NA NA NA NA
2 22289755 2925168 48 0.58780724 NA NA NA NA
5 NA 2925168 64 NA 0.32866667 NA NA NA
3 22289756 2925168 72 0.20293065 NA NA NA 0.20293065
51 NA 40876220 7 NA 1.11575000 NA NA NA
11 22289571 40876220 16 0.49544432 NA NA NA NA
21 22289572 40876220 24 0.36517471 NA NA NA NA
31 22289573 40876220 48 0.14622386 NA NA NA NA
6 NA 40876220 64 NA 0.08246667 NA NA NA
41 22289574 40876220 72 0.05855120 NA NA NA 0.05855120
42 NA 63536292 7 NA 1.03375000 NA NA NA
12 22289531 63536292 12 0.51896071 NA NA NA NA
22 22289532 63536292 16 0.36933771 NA NA NA NA
32 22289533 63536292 24 0.18706907 NA NA NA 0.18706907
52 NA 63536292 64 NA 0.00000000 NA NA NA
43 NA 111299859 7 NA 1.31050000 NA NA NA
13 22289597 111299859 16 0.58377725 NA NA NA NA
23 22289598 111299859 24 0.37399383 NA NA NA NA
33 22289599 111299859 48 0.09833684 NA NA NA 0.09833684
53 NA 111299859 64 NA 0.03250000 NA NA NA
54 NA 112146587 7 NA 2.98750000 NA NA NA
14 22289765 112146587 12 1.59483827 NA NA NA NA
24 22289766 112146587 16 1.14287770 NA NA NA NA
34 22289767 112146587 24 0.58690253 NA NA NA NA
44 22289768 112146587 48 0.07948124 NA NA NA 0.07948124
61 NA 112146587 64 NA 0.02586667 NA NA NA
SDEID AUC0_7 AUC8_48 AUC0_64
1 111299859 5.150750 16.115864 23.59851
2 112146587 16.630430 27.186570 47.51955
3 123811839 1826.885933 1140.509935 3155.39210
4 145593586 759.772228 1482.006413 2543.66350
5 202367199 6447.260530 5524.243972 12915.26200
6 233891605 2026.373649 1432.192203 3683.11590
7 257914734 16.023791 46.488059 73.48402
8 2925168 19.037500 62.309949 91.99822
9 355441192 395.187556 414.853089 883.42274
10 40876220 3.982875 15.775430 22.50988
11 409149207 4278.933737 2468.641048 7170.07381
12 427589065 7730.206336 5323.498495 13984.33804
13 443820693 11.910000 51.133415 70.47629
14 463304641 94.077277 145.965750 268.55542
AUC.1.T1 AUC.1.T2 AUC.2.T1 AUC.2.T2 AUC.3.T1 AUC.3.T2 AUC.4.T1 AUC.4.T2
1 0 7 8 48 0 64 12 130
PKDATAROWID SDEID TIME CEST_KEL CEST_INT CEST_EXT CEST_C0 CEST_TLAST
4 NA 2925168 7 NA 3.59500000 NA NA NA
1 22289754 2925168 24 1.70263761 NA NA NA NA
2 22289755 2925168 48 0.58780724 NA NA NA NA
5 NA 2925168 64 NA 0.32866667 NA NA NA
3 22289756 2925168 72 0.20293065 NA NA NA 0.20293065
6 NA 2925168 130 NA NA 1.552816e-02 NA NA
51 NA 40876220 7 NA 1.11575000 NA NA NA
11 22289571 40876220 16 0.49544432 NA NA NA NA
21 22289572 40876220 24 0.36517471 NA NA NA NA
31 22289573 40876220 48 0.14622386 NA NA NA NA
61 NA 40876220 64 NA 0.08246667 NA NA NA
41 22289574 40876220 72 0.05855120 NA NA NA 0.05855120
7 NA 40876220 130 NA NA 6.411404e-03 NA NA
42 NA 63536292 7 NA 1.03375000 NA NA NA
12 22289531 63536292 12 0.51896071 NA NA NA NA
22 22289532 63536292 16 0.36933771 NA NA NA NA
32 22289533 63536292 24 0.18706907 NA NA NA 0.18706907
52 NA 63536292 64 NA 0.00000000 NA NA NA
62 NA 63536292 130 NA NA 2.278583e-05 NA NA
43 NA 111299859 7 NA 1.31050000 NA NA NA
13 22289597 111299859 16 0.58377725 NA NA NA NA
23 22289598 111299859 24 0.37399383 NA NA NA NA
33 22289599 111299859 48 0.09833684 NA NA NA 0.09833684
53 NA 111299859 64 NA 0.03250000 NA NA NA
63 NA 111299859 130 NA NA 1.024574e-03 NA NA
54 NA 112146587 7 NA 2.98750000 NA NA NA
14 22289765 112146587 12 1.59483827 NA NA NA NA
24 22289766 112146587 16 1.14287770 NA NA NA NA
34 22289767 112146587 24 0.58690253 NA NA NA NA
44 22289768 112146587 48 0.07948124 NA NA NA 0.07948124
64 NA 112146587 64 NA 0.02586667 NA NA NA
71 NA 112146587 130 NA NA 8.581603e-05 NA NA
SDEID AUC0_7 AUC8_48 AUC0_64 AUC12_130
1 111299859 5.150750 16.115864 23.59851 12.812288
2 112146587 16.630430 27.186570 47.51955 19.245042
3 123811839 1826.885933 1140.509935 3155.39210 832.210865
4 145593586 759.772228 1482.006413 2543.66350 1602.216675
5 202367199 6447.260530 5524.243972 12915.26200 4355.547472
6 233891605 2026.373649 1432.192203 3683.11590 1078.280104
7 257914734 16.023791 46.488059 73.48402 53.516499
8 2925168 19.037500 62.309949 91.99822 63.098301
9 355441192 395.187556 414.853089 883.42274 353.383998
10 40876220 3.982875 15.775430 22.50988 15.219666
11 409149207 4278.933737 2468.641048 7170.07381 1770.280370
12 427589065 7730.206336 5323.498495 13984.33804 3981.989941
13 443820693 11.910000 51.133415 70.47629 51.239667
14 463304641 94.077277 145.965750 268.55542 146.083234
15 471081571 77.181437 108.658798 204.54646 101.591513
16 499820574 5228.563212 4459.884946 10432.14093 3657.863653
17 500091016 22.587594 61.264286 93.30063 57.757705
18 500805530 471.496268 542.503402 1117.77753 486.659083
19 503192693 3620.713623 3105.414236 7252.21114 2605.480406
20 507682703 53.298750 131.477784 203.91558 101.097514
21 518707064 6670.725819 4731.692389 12129.35766 4005.987642
22 524844737 1628.484634 1407.499421 3307.33363 1224.938501
23 539272807 209.312058 332.789228 604.37115 297.353704
24 565486844 7445.501574 5909.793566 14477.65743 5877.935706
25 577177821 1055.339216 1175.596164 2437.80067 962.091893
26 579271281 4400.426105 3558.197713 8568.68723 2604.478526
27 582963447 41.346250 154.878916 221.93726 137.869401
28 621374373 7451.020059 6656.170696 15160.12434 5251.671038
29 63536292 4.869875 9.248423 15.17517 6.130309
30 652003114 1290.255720 1627.226746 3215.16872 1587.397767
31 668240634 4569.409480 3585.059094 8754.50885 3009.311355
32 670348158 405.622354 588.225438 1114.30067 668.916118
33 732449590 840.800000 2728.955981 4087.42371 3045.073255
34 754174517 1252.107866 1828.834087 3373.54061 1658.022511
35 759109991 1681.984359 1188.749998 3068.01559 946.023432
36 773547601 55.151957 191.942856 277.91700 200.581554
37 822445112 6.878162 11.650839 20.89284 12.635232
38 832770130 391.805441 946.630254 1508.28984 964.916176
39 836945347 2142.867553 1753.192912 4159.07458 1384.564832
40 908214575 110.519647 228.236251 377.13650 213.323090
41 919470224 3371.716409 4370.179733 8514.10003 3735.307241
42 921404682 493.469246 499.497906 1072.45419 403.226382
43 922793085 269.998844 458.234651 805.44510 409.614595
44 927491002 1103.458808 1334.859976 2665.03729 1159.199149
45 935549083 10.641990 21.250117 36.06401 21.305225
46 949633293 18.197500 44.909422 70.60103 38.855472
47 954855373 5402.856051 3347.044867 9289.05440 2365.879252
48 978075228 9.329302 14.832649 26.59689 12.390984
Note that this fails in a crash. The crash is not acceptable. While it's not required to support partial AUCs with both points extrapolated, it's reasonable to expect that there is no reason not to support this use case and it's comletely unacceptable that it causes a crash while processing the data. It is an expectation that the computation engine should handle such cases (all numerical computations) in a way that if the computation is numerical infeasible or not consistent with the computation of a PK parameter, the code should indicate that the parameter is not able to be computed by creating a missing value, i.e. an "NA" but not cause a code crash or stop.
AUC.1.T1 AUC.1.T2 AUC.2.T1 AUC.2.T2 AUC.3.T1 AUC.3.T2 AUC.4.T1 AUC.4.T2 AUC.5.T1 AUC.5.T2
1 0 7 8 48 0 64 12 130 130 140
> results_list <- run_computation(data=d, map=mct, flag=flags, parameterset=parameterset)
Error in value[[3L]](cond) : Error in tmp[[2]]: subscript out of bounds
For SDEID 2925168
In addition: Warning messages:
1: In run_M1_SD_computation(data = merged_data, map = map_data, method = method, :
Flag 'FLGACCEPTTAU' cannot be computed if 'TAUi' is not provided
2: In run_M1_SD_computation(data = merged_data, map = map_data, method = method, :
Show Traceback
Rerun with Debug
Error in value[[3L]](cond) : Error in tmp[[2]]: subscript out of bounds
For SDEID 2925168
AUC.1.T1 AUC.1.T2 AUC.2.T1 AUC.2.T2 AUC.3.T1 AUC.3.T2 AUC.4.T1 AUC.4.T2 AUC.5.T1 AUC.5.T2
1 0 7 8 48 0 64 12 130 6 20
PKDATAROWID SDEID TIME CEST_KEL CEST_INT CEST_EXT CEST_C0 CEST_TLAST
7 NA 2925168 6 NA 3.590000e+00 NA NA NA
4 NA 2925168 7 NA 3.595000e+00 NA NA NA
8 NA 2925168 20 NA 1.970000e+00 NA NA NA
1 22289754 2925168 24 1.70263761 NA NA NA NA
2 22289755 2925168 48 0.58780724 NA NA NA NA
5 NA 2925168 64 NA 3.286667e-01 NA NA NA
3 22289756 2925168 72 0.20293065 NA NA NA 0.20293065
6 NA 2925168 130 NA NA 1.552816e-02 NA NA
81 NA 40876220 6 NA 9.315000e-01 NA NA NA
51 NA 40876220 7 NA 1.115750e+00 NA NA NA
11 22289571 40876220 16 0.49544432 NA NA NA NA
9 NA 40876220 20 NA 4.580000e-01 NA NA NA
21 22289572 40876220 24 0.36517471 NA NA NA NA
31 22289573 40876220 48 0.14622386 NA NA NA NA
61 NA 40876220 64 NA 8.246667e-02 NA NA NA
41 22289574 40876220 72 0.05855120 NA NA NA 0.05855120
71 NA 40876220 130 NA NA 6.411404e-03 NA NA
72 NA 63536292 6 NA 9.875000e-01 NA NA NA
42 NA 63536292 7 NA 1.033750e+00 NA NA NA
12 22289531 63536292 12 0.51896071 NA NA NA NA
22 22289532 63536292 16 0.36933771 NA NA NA NA
82 NA 63536292 20 NA 2.690000e-01 NA NA NA
32 22289533 63536292 24 0.18706907 NA NA NA 0.18706907
52 NA 63536292 64 NA 0.000000e+00 NA NA NA
62 NA 63536292 130 NA NA 2.278583e-05 NA NA
73 NA 111299859 6 NA 1.161000e+00 NA NA NA
43 NA 111299859 7 NA 1.310500e+00 NA NA NA
13 22289597 111299859 16 0.58377725 NA NA NA NA
83 NA 111299859 20 NA 4.780000e-01 NA NA NA
23 22289598 111299859 24 0.37399383 NA NA NA NA
33 22289599 111299859 48 0.09833684 NA NA NA 0.09833684
53 NA 111299859 64 NA 3.250000e-02 NA NA NA
63 NA 111299859 130 NA NA 1.024574e-03 NA NA
84 NA 112146587 6 NA 3.055000e+00 NA NA NA
54 NA 112146587 7 NA 2.987500e+00 NA NA NA
14 22289765 112146587 12 1.59483827 NA NA NA NA
24 22289766 112146587 16 1.14287770 NA NA NA NA
91 NA 112146587 20 NA 8.375000e-01 NA NA NA
34 22289767 112146587 24 0.58690253 NA NA NA NA
44 22289768 112146587 48 0.07948124 NA NA NA 0.07948124
64 NA 112146587 64 NA 2.586667e-02 NA NA NA
74 NA 112146587 130 NA NA 8.581603e-05 NA NA
111 NA 123811839 6 NA 1.945000e+02 NA NA NA
85 NA 123811839 7 NA 1.647500e+02 NA NA NA
15 22290452 123811839 12 44.59385840 NA NA NA NA
25 22290453 123811839 16 36.10132904 NA NA NA NA
121 NA 123811839 20 NA 3.180000e+01 NA NA NA
35 22290454 123811839 24 23.66025768 NA NA NA NA
45 22290455 123811839 48 6.66052265 NA NA NA NA
92 NA 123811839 64 NA 2.280000e+00 NA NA NA
55 22290456 123811839 72 1.87498220 NA NA NA NA
65 22290457 123811839 96 0.52782018 NA NA NA NA
75 22290458 123811839 120 0.14858495 NA NA NA 0.14858495
10 NA 123811839 130 NA NA 8.761837e-02 NA NA
86 NA 145593586 6 NA 9.995000e+01 NA NA NA
56 NA 145593586 7 NA 8.692500e+01 NA NA NA
93 NA 145593586 20 NA 4.175000e+01 NA NA NA
16 22291157 145593586 48 18.32531818 NA NA NA NA
66 NA 145593586 64 NA 1.027333e+01 NA NA NA
26 22291158 145593586 72 5.87505987 NA NA NA NA
36 22291159 145593586 96 1.88353229 NA NA NA NA
46 22291160 145593586 120 0.60385664 NA NA NA 0.60385664
76 NA 145593586 130 NA NA 3.759102e-01 NA NA
101 NA 202367199 6 NA 8.275000e+02 NA NA NA
77 NA 202367199 7 NA 7.162500e+02 NA NA NA
17 22290934 202367199 16 155.89036680 NA NA NA NA
112 NA 202367199 20 NA 1.540000e+02 NA NA NA
27 22290935 202367199 24 107.16512307 NA NA NA NA
37 22290936 202367199 48 34.81416886 NA NA NA NA
87 NA 202367199 64 NA 1.520667e+01 NA NA NA
47 22290937 202367199 72 11.30989560 NA NA NA NA
57 22290938 202367199 96 3.67418619 NA NA NA NA
67 22290939 202367199 120 1.19361351 NA NA NA 1.19361351
94 NA 202367199 130 NA NA 7.471477e-01 NA NA
102 NA 233891605 6 NA 2.475000e+02 NA NA NA
78 NA 233891605 7 NA 2.017500e+02 NA NA NA
18 22290518 233891605 16 41.50390320 NA NA NA NA
113 NA 233891605 20 NA 3.980000e+01 NA NA NA
28 22290519 233891605 24 27.33064016 NA NA NA NA
38 22290520 233891605 48 7.80427191 NA NA NA NA
88 NA 233891605 64 NA 3.320000e+00 NA NA NA
48 22290521 233891605 72 2.22851202 NA NA NA NA
58 22290522 233891605 96 0.63635223 NA NA NA NA
68 22290523 233891605 120 0.18171056 NA NA NA 0.18171056
95 NA 233891605 130 NA NA 1.077907e-01 NA NA
96 NA 257914734 6 NA 2.770000e+00 NA NA NA
69 NA 257914734 7 NA 2.820000e+00 NA NA NA
19 22289791 257914734 12 1.66768192 NA NA NA NA
29 22289792 257914734 16 1.47132691 NA NA NA NA
103 NA 257914734 20 NA 1.215000e+00 NA NA NA
39 22289793 257914734 24 1.14525209 NA NA NA NA
49 22289794 257914734 48 0.54010330 NA NA NA NA
79 NA 257914734 64 NA 3.733333e-01 NA NA NA
59 22289795 257914734 72 0.25471386 NA NA NA 0.25471386
89 NA 257914734 130 NA NA 4.141846e-02 NA NA
810 NA 355441192 6 NA 6.115000e+01 NA NA NA
510 NA 355441192 7 NA 5.107500e+01 NA NA NA
97 NA 355441192 20 NA 1.174500e+01 NA NA NA
110 22290260 355441192 48 2.64457864 NA NA NA NA
610 NA 355441192 64 NA 1.538000e+00 NA NA NA
210 22290261 355441192 72 0.96307502 NA NA NA NA
310 22290262 355441192 96 0.35072260 NA NA NA NA
410 22290263 355441192 120 0.12772249 NA NA NA 0.12772249
710 NA 355441192 130 NA NA 8.384501e-02 NA NA
114 NA 409149207 6 NA 4.605000e+02 NA NA NA
811 NA 409149207 7 NA 3.882500e+02 NA NA NA
115 22290686 409149207 12 92.37008404 NA NA NA NA
211 22290687 409149207 16 75.56168235 NA NA NA NA
122 NA 409149207 20 NA 6.920000e+01 NA NA NA
311 22290688 409149207 24 50.56408802 NA NA NA NA
411 22290689 409149207 48 15.15178707 NA NA NA NA
98 NA 409149207 64 NA 4.846667e+00 NA NA NA
511 22290690 409149207 72 4.54031034 NA NA NA NA
611 22290691 409149207 96 1.36052717 NA NA NA NA
711 22290692 409149207 120 0.40768891 NA NA NA 0.40768891
104 NA 409149207 130 NA NA 2.467485e-01 NA NA
105 NA 427589065 6 NA 1.026500e+03 NA NA NA
712 NA 427589065 7 NA 8.447500e+02 NA NA NA
116 22290960 427589065 16 140.81814484 NA NA NA NA
117 NA 427589065 20 NA 1.455000e+02 NA NA NA
212 22290961 427589065 24 96.88854127 NA NA NA NA
312 22290962 427589065 48 31.55831549 NA NA NA NA
812 NA 427589065 64 NA 1.312000e+01 NA NA NA
412 22290963 427589065 72 10.27910281 NA NA NA NA
512 22290964 427589065 96 3.34808601 NA NA NA NA
SDEID TREATXT AUC0_7 AUC8_48 AUC0_64 AUC12_130 AUC6_20
1 111299859 1 mg 5.150750 16.115864 23.59851 12.812288 12.008237
2 112146587 3 mg 16.630430 27.186570 47.51955 19.245042 23.774437
3 123811839 100 mg 1826.885933 1140.509935 3155.39210 832.210865 1071.528281
4 145593586 900 mg 759.772228 1482.006413 2543.66350 1602.216675 796.854834
5 202367199 600 mg 6447.260530 5524.243972 12915.26200 4355.547472 4800.194133
6 233891605 100 mg 2026.373649 1432.192203 3683.11590 1078.280104 1313.115532
7 257914734 3 mg 16.023791 46.488059 73.48402 53.516499 25.442785
8 2925168 3 mg 19.037500 62.309949 91.99822 63.098301 37.097428
9 355441192 30 mg 395.187556 414.853089 883.42274 353.383998 345.894467
10 40876220 1 mg 3.982875 15.775430 22.50988 15.219666 11.053262
11 409149207 300 mg 4278.933737 2468.641048 7170.07381 1770.280370 2448.882872
12 427589065 600 mg 7730.206336 5323.498495 13984.33804 3981.989941 5134.207534
13 443820693 3 mg 11.910000 51.133415 70.47629 51.239667 29.116758
14 463304641 10 mg 94.077277 145.965750 268.55542 146.083234 109.630738
15 471081571 10 mg 77.181437 108.658798 204.54646 101.591513 76.211768
16 499820574 300 mg 5228.563212 4459.884946 10432.14093 3657.863653 3803.017712
17 500091016 3 mg 22.587594 61.264286 93.30063 57.757705 36.260678
18 500805530 30 mg 471.496268 542.503402 1117.77753 486.659083 434.664441
19 503192693 300 mg 3620.713623 3105.414236 7252.21114 2605.480406 2711.409465
20 507682703 10 mg 53.298750 131.477784 203.91558 101.097514 106.434627
21 518707064 300 mg 6670.725819 4731.692389 12129.35766 4005.987642 3853.675277
22 524844737 100 mg 1628.484634 1407.499421 3307.33363 1224.938501 1187.121566
23 539272807 30 mg 209.312058 332.789228 604.37115 297.353704 264.822788
24 565486844 600 mg 7445.501574 5909.793566 14477.65743 5877.935706 4408.930076
25 577177821 100 mg 1055.339216 1175.596164 2437.80067 962.091893 1059.412730
26 579271281 600 mg 4400.426105 3558.197713 8568.68723 2604.478526 3529.223634
27 582963447 10 mg 41.346250 154.878916 221.93726 137.869401 103.445417
28 621374373 600 mg 7451.020059 6656.170696 15160.12434 5251.671038 6076.315913
29 63536292 1 mg 4.869875 9.248423 15.17517 6.130309 8.159038
30 652003114 900 mg 1290.255720 1627.226746 3215.16872 1587.397767 1210.859818
31 668240634 300 mg 4569.409480 3585.059094 8754.50885 3009.311355 3026.455685
32 670348158 30 mg 405.622354 588.225438 1114.30067 668.916118 416.936841
33 732449590 900 mg 840.800000 2728.955981 4087.42371 3045.073255 1544.921978
34 754174517 900 mg 1252.107866 1828.834087 3373.54061 1658.022511 1293.081605
35 759109991 100 mg 1681.984359 1188.749998 3068.01559 946.023432 1036.509708
36 773547601 10 mg 55.151957 191.942856 277.91700 200.581554 109.896773
37 822445112 1 mg 6.878162 11.650839 20.89284 12.635232 7.509056
38 832770130 900 mg 391.805441 946.630254 1508.28984 964.916176 537.035801
39 836945347 100 mg 2142.867553 1753.192912 4159.07458 1384.564832 1523.287556
40 908214575 10 mg 110.519647 228.236251 377.13650 213.323090 152.152089
41 919470224 300 mg 3371.716409 4370.179733 8514.10003 3735.307241 3690.125705
42 921404682 30 mg 493.469246 499.497906 1072.45419 403.226382 398.650667
43 922793085 30 mg 269.998844 458.234651 805.44510 409.614595 337.659477
44 927491002 900 mg 1103.458808 1334.859976 2665.03729 1159.199149 1014.496642
45 935549083 1 mg 10.641990 21.250117 36.06401 21.305225 14.257864
46 949633293 3 mg 18.197500 44.909422 70.60103 38.855472 31.082832
47 954855373 600 mg 5402.856051 3347.044867 9289.05440 2365.879252 3282.149978
48 978075228 1 mg 9.329302 14.832649 26.59689 12.390984 10.727837
AUC.1.T1 AUC.1.T2 AUC.2.T1 AUC.2.T2 AUC.3.T1 AUC.3.T2 AUC.4.T1 AUC.4.T2 AUC.5.T1 AUC.5.T2 AUC.6.T1 AUC.6.T2
1 0 7 8 48 0 64 12 130 6 20 60 124
PKDATAROWID SDEID TIME CEST_KEL CEST_INT CEST_EXT CEST_C0 CEST_TLAST
7 NA 2925168 6 NA 3.59000000 NA NA NA
4 NA 2925168 7 NA 3.59500000 NA NA NA
8 NA 2925168 20 NA 1.97000000 NA NA NA
1 22289754 2925168 24 1.70263761 NA NA NA NA
2 22289755 2925168 48 0.58780724 NA NA NA NA
9 NA 2925168 60 NA 0.39050000 NA NA NA
5 NA 2925168 64 NA 0.32866667 NA NA NA
3 22289756 2925168 72 0.20293065 NA NA NA 0.20293065
10 NA 2925168 124 NA NA 2.025778e-02 NA NA
6 NA 2925168 130 NA NA 1.552816e-02 NA NA
81 NA 40876220 6 NA 0.93150000 NA NA NA
51 NA 40876220 7 NA 1.11575000 NA NA NA
12 22289571 40876220 16 0.49544432 NA NA NA NA
91 NA 40876220 20 NA 0.45800000 NA NA NA
21 22289572 40876220 24 0.36517471 NA NA NA NA
31 22289573 40876220 48 0.14622386 NA NA NA NA
101 NA 40876220 60 NA 0.08985000 NA NA NA
61 NA 40876220 64 NA 0.08246667 NA NA NA
41 22289574 40876220 72 0.05855120 NA NA NA 0.05855120
11 NA 40876220 124 NA NA 8.059791e-03 NA NA
71 NA 40876220 130 NA NA 6.411404e-03 NA NA
72 NA 63536292 6 NA 0.98750000 NA NA NA
42 NA 63536292 7 NA 1.03375000 NA NA NA
13 22289531 63536292 12 0.51896071 NA NA NA NA
22 22289532 63536292 16 0.36933771 NA NA NA NA
82 NA 63536292 20 NA 0.26900000 NA NA NA
32 22289533 63536292 24 0.18706907 NA NA NA 0.18706907
92 NA 63536292 60 NA 0.00000000 NA NA NA
52 NA 63536292 64 NA 0.00000000 NA NA NA
102 NA 63536292 124 NA NA 3.795169e-05 NA NA
62 NA 63536292 130 NA NA 2.278583e-05 NA NA
73 NA 111299859 6 NA 1.16100000 NA NA NA
43 NA 111299859 7 NA 1.31050000 NA NA NA
14 22289597 111299859 16 0.58377725 NA NA NA NA
83 NA 111299859 20 NA 0.47800000 NA NA NA
23 22289598 111299859 24 0.37399383 NA NA NA NA
33 22289599 111299859 48 0.09833684 NA NA NA 0.09833684
93 NA 111299859 60 NA 0.04875000 NA NA NA
53 NA 111299859 64 NA 0.03250000 NA NA NA
103 NA 111299859 124 NA NA 1.430805e-03 NA NA
63 NA 111299859 130 NA NA 1.024574e-03 NA NA
84 NA 112146587 6 NA 3.05500000 NA NA NA
54 NA 112146587 7 NA 2.98750000 NA NA NA
15 22289765 112146587 12 1.59483827 NA NA NA NA
24 22289766 112146587 16 1.14287770 NA NA NA NA
94 NA 112146587 20 NA 0.83750000 NA NA NA
34 22289767 112146587 24 0.58690253 NA NA NA NA
44 22289768 112146587 48 0.07948124 NA NA NA 0.07948124
104 NA 112146587 60 NA 0.03880000 NA NA NA
64 NA 112146587 64 NA 0.02586667 NA NA NA
111 NA 112146587 124 NA NA 1.414633e-04 NA NA
74 NA 112146587 130 NA NA 8.581603e-05 NA NA
112 NA 123811839 6 NA 194.50000000 NA NA NA
85 NA 123811839 7 NA 164.75000000 NA NA NA
16 22290452 123811839 12 44.59385840 NA NA NA NA
25 22290453 123811839 16 36.10132904 NA NA NA NA
121 NA 123811839 20 NA 31.80000000 NA NA NA
35 22290454 123811839 24 23.66025768 NA NA NA NA
45 22290455 123811839 48 6.66052265 NA NA NA NA
131 NA 123811839 60 NA 2.88000000 NA NA NA
95 NA 123811839 64 NA 2.28000000 NA NA NA
55 22290456 123811839 72 1.87498220 NA NA NA NA
65 22290457 123811839 96 0.52782018 NA NA NA NA
75 22290458 123811839 120 0.14858495 NA NA NA 0.14858495
141 NA 123811839 124 NA NA 1.202882e-01 NA NA
105 NA 123811839 130 NA NA 8.761837e-02 NA NA
86 NA 145593586 6 NA 99.95000000 NA NA NA
56 NA 145593586 7 NA 86.92500000 NA NA NA
96 NA 145593586 20 NA 41.75000000 NA NA NA
17 22291157 145593586 48 18.32531818 NA NA NA NA
106 NA 145593586 60 NA 12.45500000 NA NA NA
66 NA 145593586 64 NA 10.27333333 NA NA NA
26 22291158 145593586 72 5.87505987 NA NA NA NA
36 22291159 145593586 96 1.88353229 NA NA NA NA
46 22291160 145593586 120 0.60385664 NA NA NA 0.60385664
113 NA 145593586 124 NA NA 4.995673e-01 NA NA
76 NA 145593586 130 NA NA 3.759102e-01 NA NA
107 NA 202367199 6 NA 827.50000000 NA NA NA
77 NA 202367199 7 NA 716.25000000 NA NA NA
18 22290934 202367199 16 155.89036680 NA NA NA NA
114 NA 202367199 20 NA 154.00000000 NA NA NA
27 22290935 202367199 24 107.16512307 NA NA NA NA
37 22290936 202367199 48 34.81416886 NA NA NA NA
122 NA 202367199 60 NA 18.30500000 NA NA NA
87 NA 202367199 64 NA 15.20666667 NA NA NA
47 22290937 202367199 72 11.30989560 NA NA NA NA
57 22290938 202367199 96 3.67418619 NA NA NA NA
67 22290939 202367199 120 1.19361351 NA NA NA 1.19361351
132 NA 202367199 124 NA NA 9.896482e-01 NA NA
97 NA 202367199 130 NA NA 7.471477e-01 NA NA
108 NA 233891605 6 NA 247.50000000 NA NA NA
78 NA 233891605 7 NA 201.75000000 NA NA NA
19 22290518 233891605 16 41.50390320 NA NA NA NA
115 NA 233891605 20 NA 39.80000000 NA NA NA
28 22290519 233891605 24 27.33064016 NA NA NA NA
38 22290520 233891605 48 7.80427191 NA NA NA NA
123 NA 233891605 60 NA 4.08000000 NA NA NA
88 NA 233891605 64 NA 3.32000000 NA NA NA
48 22290521 233891605 72 2.22851202 NA NA NA NA
58 22290522 233891605 96 0.63635223 NA NA NA NA
68 22290523 233891605 120 0.18171056 NA NA NA 0.18171056
133 NA 233891605 124 NA NA 1.474554e-01 NA NA
98 NA 233891605 130 NA NA 1.077907e-01 NA NA
99 NA 257914734 6 NA 2.77000000 NA NA NA
69 NA 257914734 7 NA 2.82000000 NA NA NA
110 22289791 257914734 12 1.66768192 NA NA NA NA
29 22289792 257914734 16 1.47132691 NA NA NA NA
109 NA 257914734 20 NA 1.21500000 NA NA NA
39 22289793 257914734 24 1.14525209 NA NA NA NA
49 22289794 257914734 48 0.54010330 NA NA NA NA
116 NA 257914734 60 NA 0.44800000 NA NA NA
79 NA 257914734 64 NA 0.37333333 NA NA NA
59 22289795 257914734 72 0.25471386 NA NA NA 0.25471386
124 NA 257914734 124 NA NA 4.998043e-02 NA NA
89 NA 257914734 130 NA NA 4.141846e-02 NA NA
810 NA 355441192 6 NA 61.15000000 NA NA NA
510 NA 355441192 7 NA 51.07500000 NA NA NA
910 NA 355441192 20 NA 11.74500000 NA NA NA
117 22290260 355441192 48 2.64457864 NA NA NA NA
1010 NA 355441192 60 NA 1.82600000 NA NA NA
610 NA 355441192 64 NA 1.53800000 NA NA NA
210 22290261 355441192 72 0.96307502 NA NA NA NA
310 22290262 355441192 96 0.35072260 NA NA NA NA
410 22290263 355441192 120 0.12772249 NA NA NA 0.12772249
118 NA 355441192 124 NA NA 1.079323e-01 NA NA
SDEID TREATXT AUC0_7 AUC8_48 AUC0_64 AUC12_130 AUC6_20 AUC60_124
1 111299859 1 mg 5.150750 16.115864 23.59851 12.812288 12.008237 3.297009e-01
2 112146587 3 mg 16.630430 27.186570 47.51955 19.245042 23.774437 2.364780e-01
3 123811839 100 mg 1826.885933 1140.509935 3155.39210 832.210865 1071.528281 4.724301e+01
4 145593586 900 mg 759.772228 1482.006413 2543.66350 1602.216675 796.854834 2.141581e+02
5 202367199 600 mg 6447.260530 5524.243972 12915.26200 4355.547472 4800.194133 3.486005e+02
6 233891605 100 mg 2026.373649 1432.192203 3683.11590 1078.280104 1313.115532 6.823288e+01
7 257914734 3 mg 16.023791 46.488059 73.48402 53.516499 25.442785 9.910584e+00
8 2925168 3 mg 19.037500 62.309949 91.99822 63.098301 37.097428 7.604945e+00
9 355441192 30 mg 395.187556 414.853089 883.42274 353.383998 345.894467 3.612597e+01
10 40876220 1 mg 3.982875 15.775430 22.50988 15.219666 11.053262 2.396276e+00
11 409149207 300 mg 4278.933737 2468.641048 7170.07381 1770.280370 2448.882872 1.152641e+02
12 427589065 600 mg 7730.206336 5323.498495 13984.33804 3981.989941 5134.207534 3.071642e+02
13 443820693 3 mg 11.910000 51.133415 70.47629 51.239667 29.116758 5.988614e+00
14 463304641 10 mg 94.077277 145.965750 268.55542 146.083234 109.630738 2.669005e+01
15 471081571 10 mg 77.181437 108.658798 204.54646 101.591513 76.211768 1.093362e+01
16 499820574 300 mg 5228.563212 4459.884946 10432.14093 3657.863653 3803.017712 3.534637e+02
17 500091016 3 mg 22.587594 61.264286 93.30063 57.757705 36.260678 5.072884e+00
18 500805530 30 mg 471.496268 542.503402 1117.77753 486.659083 434.664441 5.875573e+01
19 503192693 300 mg 3620.713623 3105.414236 7252.21114 2605.480406 2711.409465 2.793451e+02
20 507682703 10 mg 53.298750 131.477784 203.91558 101.097514 106.434627 5.418666e+00
21 518707064 300 mg 6670.725819 4731.692389 12129.35766 4005.987642 3853.675277 3.746088e+02
22 524844737 100 mg 1628.484634 1407.499421 3307.33363 1224.938501 1187.121566 1.558447e+02
23 539272807 30 mg 209.312058 332.789228 604.37115 297.353704 264.822788 3.774075e+01
24 565486844 600 mg 7445.501574 5909.793566 14477.65743 5877.935706 4408.930076 9.551179e+02
25 577177821 100 mg 1055.339216 1175.596164 2437.80067 962.091893 1059.412730 1.011227e+02
26 579271281 600 mg 4400.426105 3558.197713 8568.68723 2604.478526 3529.223634 1.955577e+02
27 582963447 10 mg 41.346250 154.878916 221.93726 137.869401 103.445417 1.188009e+01
28 621374373 600 mg 7451.020059 6656.170696 15160.12434 5251.671038 6076.315913 4.695618e+02
29 63536292 1 mg 4.869875 9.248423 15.17517 6.130309 8.159038 9.867439e-04
30 652003114 900 mg 1290.255720 1627.226746 3215.16872 1587.397767 1210.859818 2.560997e+02
31 668240634 300 mg 4569.409480 3585.059094 8754.50885 3009.311355 3026.455685 3.204506e+02
32 670348158 30 mg 405.622354 588.225438 1114.30067 668.916118 416.936841 1.687498e+02
33 732449590 900 mg 840.800000 2728.955981 4087.42371 3045.073255 1544.921978 5.612106e+02
34 754174517 900 mg 1252.107866 1828.834087 3373.54061 1658.022511 1293.081605 1.717519e+02
35 759109991 100 mg 1681.984359 1188.749998 3068.01559 946.023432 1036.509708 7.730765e+01
36 773547601 10 mg 55.151957 191.942856 277.91700 200.581554 109.896773 2.861341e+01
37 822445112 1 mg 6.878162 11.650839 20.89284 12.635232 7.509056 2.386116e+00
38 832770130 900 mg 391.805441 946.630254 1508.28984 964.916176 537.035801 1.051958e+02
39 836945347 100 mg 2142.867553 1753.192912 4159.07458 1384.564832 1523.287556 1.061628e+02
40 908214575 10 mg 110.519647 228.236251 377.13650 213.323090 152.152089 2.116397e+01
41 919470224 300 mg 3371.716409 4370.179733 8514.10003 3735.307241 3690.125705 3.931082e+02
42 921404682 30 mg 493.469246 499.497906 1072.45419 403.226382 398.650667 2.730232e+01
43 922793085 30 mg 269.998844 458.234651 805.44510 409.614595 337.659477 3.903040e+01
44 927491002 900 mg 1103.458808 1334.859976 2665.03729 1159.199149 1014.496642 9.812001e+01
45 935549083 1 mg 10.641990 21.250117 36.06401 21.305225 14.257864 3.128326e+00
46 949633293 3 mg 18.197500 44.909422 70.60103 38.855472 31.082832 2.812116e+00
47 954855373 600 mg 5402.856051 3347.044867 9289.05440 2365.879252 3282.149978 1.405599e+02
48 978075228 1 mg 9.329302 14.832649 26.59689 12.390984 10.727837 4.683784e-01
This WAS working appropriately in current master branch.
d$PKCNCN <- rep(NA, nrow(d)) # computation engine should not FALL OVER
> results_list <- run_computation(data=d, map=mct, flag=flags, parameterset=parameterset)
Error in value[[3L]](cond) : Error in tmp[[2]]: subscript out of bounds
For SDEID 2925168
In addition: Warning messages:
1: In run_M1_SD_computation(data = merged_data, map = map_data, method = method, :
Flag 'FLGACCEPTTAU' cannot be computed if 'TAUi' is not provided
2: In run_M1_SD_computation(data = merged_data, map = map_data, method = method, :
Show Traceback
Rerun with Debug
Error in value[[3L]](cond) : Error in tmp[[2]]: subscript out of bounds
For SDEID 2925168
When asking for partial AUCs from x to an extrapolated point, values for CEST_EXT are reported as the same value if they share x in their partial AUC. Additionally there seems to be some IDs which cause some interpolated or extrapolated values to not be reported at all.
Hopefully the outputs below can help make sense of this issue. Understandably, there are a lot of different partial AUCs that I have not tested.
When running tc010 with additional partial AUCs from 0 to 120, 60 to 122, 60 to 144, 0 to 205 (where x = 0 or 60) and 24 to 124 (24 being an existing point, 124 being extrapolated) following output is given for SDEID == 2925168:
PKDATAROWID SDEID TIME CEST_KEL CEST_INT CEST_EXT CEST_C0 CEST_TLAST
1 NA 2925168 7 NA 3.5950000 NA NA NA
2 22289754 2925168 24 1.7026376 NA NA NA NA
3 22289755 2925168 48 0.5878072 NA NA NA NA
4 NA 2925168 64 NA 0.3286667 NA NA NA
5 22289756 2925168 72 0.2029306 NA NA NA 0.2029306
6 NA 2925168 120 NA NA 0.2837817 NA NA
7 NA 2925168 60 NA 0.3905000 NA NA NA
8 NA 2925168 122 NA NA 0.2050000 NA NA
9 NA 2925168 144 NA NA 0.2050000 NA NA
10 NA 2925168 205 NA NA 0.2837817 NA NA
11 NA 2925168 124 NA NA 0.2029306 NA NA
As can be seen the value for CEST_EXT is the same for both 120 and 205 are the same, which shared the same lower value for their partial AUCs (x = 0), while 122 and 144 are the same (x = 60).
This is not expected behaviour as it should be that the CEST_EXT value for each time point decreases and the time gets larger.
Additionally, it shows that CEST_TLAST at TIME == 72 is lower than the CEST at TIME == 120. This may be intentional, but just raising it as a potential problem if it isn't intentional.
Despite this, the partial AUCs appear to be working correctly:
AUC0_7 AUC8_48 AUC0_64 AUC0_120 AUC60_122 AUC60_144 AUC0_205 AUC24_124
8 19.0375 62.30995 91.99822 105.1922 13.87095 18.45428 125.7842 44.32238
For the same test case as above looking at SDEID == 63536292 the following output is given:
PKDATAROWID SDEID TIME CEST_KEL CEST_INT CEST_EXT CEST_C0 CEST_TLAST
24 NA 63536292 7 NA 1.03375 NA NA NA
25 22289531 63536292 12 0.5189607 NA NA NA NA
26 NA 63536292 64 NA 0.00000 NA NA NA
27 NA 63536292 120 NA NA 0.2842993 NA NA
28 NA 63536292 60 NA 0.00000 NA NA NA
29 NA 63536292 205 NA NA 0.2842993 NA NA
30 22289532 63536292 16 0.3693377 NA NA NA NA
31 22289533 63536292 24 0.1870691 NA NA NA 0.1870691
In this output:
The associated value in data_out was:
AUC0_7 AUC8_48 AUC0_64 AUC0_120 AUC60_122 AUC60_144 AUC0_205 AUC24_124
29 4.869875 9.248423 15.17517 21.99836 0 0 34.08108 2.292
The AUC from 60 to 122 and 60 to 144 may be very small, so this could be correct.
#### Clean test environment ---------------------------------------------- ####
# Clean environment
rm(params, conc, vlist, results, results_list, r, e, mct, d, flags, p, klist, n,
ninc, nexc, k, rlist, dlist, nlist, slist, df, xmin, xmax, dk, flag_data,
est_data, outputdir)
profileid <- "SDEID"
args = commandArgs(trailingOnly=TRUE)
params = c()
#### Test Case 010 ------------------------------------------------------- ####
# M1 SD
# p <- "C:/Users/tensfeldt/Box Sync/EQuIP Computation Engine/Rudraya_executed_test_cases/tc010"
testcase.id <- "tc010"
p <- "testcase/tc010_M1SD"
params["INPUT1"] = file.path(p, "B1261002.in.csv")
params["parameterset"] ="PARAMETERLIST"
params["map"] = file.path(p, "tc010_mct_updated.csv")
params["flags"] = file.path(p, "B1261002_flag_data.in.csv")
params["OUTPUT"] ="."
print(params)
mct <- read_csv(params["map"])
write_file(toJSON(mct, na="string"), path=file.path(p, "tc010_mct_updated.json"))
params["map"] = file.path(p, "tc010_mct_updated.json")
flags <- read_csv(params["flags"])
write_file(toJSON(flags, na="string"), path=file.path(p, "tc010_flags.json"))
params["flags"] = file.path(p, "tc010_flags.json")
#### Read full concentration dataset ------------------------------------- ####
# Read conc
conc <- read.csv(file=params["INPUT1"], header=TRUE, sep=",", as.is=TRUE)
vlist <- c("PKDATAROWID", "PKPTMS", "PKCNCN", "PERIOD", "TREATXT")
# Read mct
mct <- params["map"]
###mct <- read_file(mct)
###mct <- gsub("[\\]+", "", mct)
mct <- fromJSON(mct)
# Read flags
flags <- params["flags"]
###flags <- read_file(flags)
###flags <- gsub("[\\]+", "", flags)
flags <- fromJSON(flags)
# ParameterSet
if(is.na(params["parameterset"])) { params["parameterset"] <- "PARAMETERDISPLAYLIST" }
parameterset <- params["parameterset"]
# FLAGS is a subset for the specific profile, subset concentration dataframe
# to those records by PKDATAROWID
d <- conc[is.element(conc$PKDATAROWID, flags$PKDATAROWID),]
# Add in missing required parameters for virtual compute call
klist <- c("KEL", "KELRSQ", "KELNOPT", "THALFF")
if(!is.element("PARAMETERDISPLAYLIST", names(mct))) { mct$PARAMETERDISPLAYLIST <- "" }
for(i in klist) {
###if(regexpr(i, mct$PARAMETERDISPLAYLIST, ignore.case=TRUE)==-1) { mct$PARAMETERDISPLAYLIST <- c(mct$PARAMETERDISPLAYLIST, i) }
if(regexpr(i, mct$PARAMETERDISPLAYLIST, ignore.case=TRUE)==-1) { mct$PARAMETERDISPLAYLIST <- paste(mct$PARAMETERDISPLAYLIST, i, sep=";") }
}
# Remove any leading semi-colons ";"
mct$PARAMETERDISPLAYLIST <- gsub("^;*", "", mct$PARAMETERDISPLAYLIST, ignore.case=TRUE, perl=TRUE)
# For virtual compute call only: Subset the parameters to be calculated to those in parameterdisplaylist
if(!is.element("PARAMETERLIST", names(mct))) { mct$PARAMETERLIST <- "" }
# Check whether TIME selection has been set in MCT - default to NOMINAL
if(!is.element("TIME", names(mct))) { mct$TIME <- "NOMINAL" }
# Determine whether there are any points selected for inclusion in the terminal elimination phase via
# FLGEXKEL (0=not excluded/included; 1=excluded/not included)
# ***What happens if there are no flags provided?***
n <- factor(flags$FLGEXKEL, levels=c(0,1))
ninc <- length(n[n==0])
nexc <- length(n[n==1])
#### Final changes to test ----------------------------------------------- ####
### Add partial AUC
mct$AUC.4.T1 <- 0
mct$AUC.4.T2 <- 120
mct$AUC.5.T1 <- 60
mct$AUC.5.T2 <- 122
mct$AUC.6.T1 <- 60
mct$AUC.6.T2 <- 144
mct$AUC.7.T1 <- 0
mct$AUC.7.T2 <- 205
mct$AUC.8.T1 <- 24
mct$AUC.8.T2 <- 124
#### Run computation engine ---------------------------------------------- ####
### Run computation
results_list <- run_computation(data = d, map = mct, flag = flags,
parameterset = parameterset)
### Save results to objects
r <- results_list$data_out
e <- results_list$est_data
f <- results_list$flag_data
### View output
e[e$SDEID == 2925168, ]
r[r$SDEID == 2925168, c("AUC0_7", "AUC8_48", "AUC0_64", "AUC0_120", "AUC60_122", "AUC60_144", "AUC0_205", "AUC24_124")]
e[e$SDEID == 63536292, ]
r[r$SDEID == 63536292, c("AUC0_7", "AUC8_48", "AUC0_64", "AUC0_120", "AUC60_122", "AUC60_144", "AUC0_205", "AUC24_124")]
There are specific cases in commit a06c1c7 where an error occurs when asking for partial AUCs. I am not certain why this error occurs for some partial AUCs and not others. The error and traceback for an example using tc010 has been included below.
Error in value[[3L]](cond) : Error in tmp[[2]]: subscript out of bounds
For SDEID 63536292
In addition: Warning messages:
1: In run_M1_SD_computation(data = merged_data, map = map_data, method = method, :
Flag 'FLGACCEPTTAU' cannot be computed if 'TAUi' is not provided
2: In run_M1_SD_computation(data = merged_data, map = map_data, method = method, :
Error in value[[3L]](cond) : Error in tmp[[2]]: subscript out of bounds
For SDEID 63536292
7. stop(paste0(e, "For SDEID ", unique(data_data[, map_data$SDEID])[i])) at run_M1_SD_computation.R#1698
6. value[[3L]](cond)
5. tryCatchOne(expr, names, parentenv, handlers[[1L]])
4. tryCatchList(expr, classes, parentenv, handlers)
3. tryCatch({
tmp_df <- data_data[data_data[, map_data$SDEID] == unique(data_data[,
map_data$SDEID])[i], ]
tmp_df <- tmp_df[order(tmp_df[, map_data$TIME]), ] ... at run_M1_SD_computation.R#817
2. run_M1_SD_computation(data = merged_data, map = map_data, method = method,
model_regex = model_regex, parameter_list = parameter_list,
return_list = return_list) at run_computation.R#2217
1. run_computation(data = d, map = mct, flag = flags, parameterset = parameterset)
The error can be replicated by adding an additional partial AUC to test case 010 using the code below:
### Add partial AUC
mct$AUC.4.T1 <- 25
mct$AUC.4.T2 <- 48
The error does not occur when mct$AUC.4.T1 is given a value of 23 or 24. mct$AUC.4.T2 appears to have some impact on the error, changing the SDEID which causes the error. A value of 144 results in SDEID 63536292 causing the error, while a value of 48 results in SDEID 2925168 causing the error
#### Final changes to test ----------------------------------------------- ####
### Add partial AUC
mct$AUC.4.T1 <- 25
mct$AUC.4.T2 <- 48
#### Run computation engine ---------------------------------------------- ####
# Run computation
results_list <- run_computation(data = d, map = mct, flag = flags,
parameterset = parameterset)
If mct$FLGNOCMAX==1 (or FLGNOCMAX==0 it turns out) (i.e. prohibit CMAX from being incorporated into KEL selections through the optimizekel routine) the following error occurs with tc010:
> results_list <- run_computation(data=d, map=mct, flag=flags, parameterset=parameterset)
Error in value[[3L]](cond) :
Error in if (flg_no_cmax) {: missing value where TRUE/FALSE needed
For SDEID 2925168
In addition: Warning messages:
1: In run_M1_SD_computation(data = merged_data, map = map_data, method = method, :
Flag 'FLGACCEPTTAU' cannot be computed if 'TAUi' is not provided
2: In run_M1_SD_computation(data = merged_data, map = map_data, method = method, :
Show Traceback
Rerun with Debug
Error in value[[3L]](cond) :
Error in if (flg_no_cmax) {: missing value where TRUE/FALSE needed
For SDEID 2925168
Not that if mct$OPTIMIZEKEL == 0 (i.e. do not optimize), this error doesn't occur even with mct$FLGNOCMAX==1.
Note that if mct$NOCMAX is included at all, the optimization routine fails with this error.
ran with both mct$optimizekel = 0 & 1, ran as expected in both cases
confirmation of whether the optimization worked completely as expected was not undertaken but will be evaluated next
need to confirm whether changes to MCT$FLGACCEPTKELCRIT are respected as well
these errors were confirmed to be pre-existing in the master code branch
Error in withCallingHandlers(expr, warning = function(w) invokeRestart("muffleWarning")) :
(list) object cannot be coerced to type 'double'
In addition: There were 50 or more warnings (use warnings() to see the first 50)
warnings()
Warning messages:
1: In runM1SScomputation(data = mergeddata, map = mapdata, … :
Flag 'FLGEMESIS' is not present in the dataset
2: In unitconversion(data = datadata, map = mapdata, result = datadata, … :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
3: In unitconversion(data = datadata, map = mapdata, result = datadata, … :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
4: In unitconversion(data = datadata, map = mapdata, result = datadata, … :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
5: In matrix(value, n, p) :
data length [157] is not a sub-multiple or multiple of the number of columns [156]
6: In unitconversion(data = datadata, map = mapdata, result = datadata, … :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
7: In unitconversion(data = datadata, map = mapdata, result = datadata, … :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
8: In unitconversion(data = datadata, map = mapdata, result = datadata, … :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
9: In matrix(value, n, p) :
data length [157] is not a sub-multiple or multiple of the number of columns [156]
10: In unitconversion(data = datadata, map = mapdata, result = datadata, … :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
11: In unitconversion(data = datadata, map = mapdata, result = datadata, … :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
12: In unitconversion(data = datadata, map = mapdata, result = datadata, … :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
13: In matrix(value, n, p) :
data length [157] is not a sub-multiple or multiple of the number of columns [156]
14: In unitconversion(data = datadata, map = mapdata, result = datadata, … :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
15: In unitconversion(data = datadata, map = mapdata, result = datadata, … :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
16: In unitconversion(data = datadata, map = mapdata, result = datadata, … :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
17: In matrix(value, n, p) :
data length [157] is not a sub-multiple or multiple of the number of columns [156]
18: In unitconversion(data = datadata, map = mapdata, result = datadata, … :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
19: In unitconversion(data = datadata, map = mapdata, result = datadata, … :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
20: In unitconversion(data = datadata, map = mapdata, result = datadata, … :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
21: In matrix(value, n, p) :
data length [157] is not a sub-multiple or multiple of the number of columns [156]
22: In unitconversion(data = datadata, map = mapdata, result = datadata, … :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
23: In unitconversion(data = datadata, map = mapdata, result = datadata, … :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
24: In unitconversion(data = datadata, map = mapdata, result = datadata, … :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
25: In matrix(value, n, p) :
data length [157] is not a sub-multiple or multiple of the number of columns [156]
26: In unitconversion(data = datadata, map = mapdata, result = datadata, … :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
27: In unitconversion(data = datadata, map = mapdata, result = datadata, … :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
28: In unitconversion(data = datadata, map = mapdata, result = datadata, … :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
29: In matrix(value, n, p) :
data length [157] is not a sub-multiple or multiple of the number of columns [156]
30: In unitconversion(data = datadata, map = mapdata, result = datadata, … :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
31: In unitconversion(data = datadata, map = mapdata, result = datadata, … :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
32: In unitconversion(data = datadata, map = mapdata, result = datadata, … :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
33: In matrix(value, n, p) :
data length [157] is not a sub-multiple or multiple of the number of columns [156]
34: In unitconversion(data = datadata, map = mapdata, result = datadata, … :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
35: In unitconversion(data = datadata, map = mapdata, result = datadata, … :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
36: In unitconversion(data = datadata, map = mapdata, result = datadata, … :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
37: In matrix(value, n, p) :
data length [157] is not a sub-multiple or multiple of the number of columns [156]
38: In unitconversion(data = datadata, map = mapdata, result = datadata, … :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
39: In unitconversion(data = datadata, map = mapdata, result = datadata, … :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
40: In unitconversion(data = datadata, map = mapdata, result = datadata, … :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
41: In matrix(value, n, p) :
data length [157] is not a sub-multiple or multiple of the number of columns [156]
42: In unitconversion(data = datadata, map = mapdata, result = datadata, … :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
43: In unitconversion(data = datadata, map = mapdata, result = datadata, … :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
44: In unitconversion(data = datadata, map = mapdata, result = datadata, … :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
45: In matrix(value, n, p) :
data length [157] is not a sub-multiple or multiple of the number of columns [156]
46: In unitconversion(data = datadata, map = mapdata, result = datadata, … :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
47: In unitconversion(data = datadata, map = mapdata, result = datadata, … :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
48: In unitconversion(data = datadata, map = mapdata, result = datadata, … :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
49: In matrix(value, n, p) :
data length [157] is not a sub-multiple or multiple of the number of columns [156]
50: In unitconversion(data = datadata, map = mapdata, result = datadata, … :
'CONCOUTPUTUNIT' is not present in the proper form! Please try again using 'Amount/Volume' format!
to test further
and no regression was performed apparently when executing runcomputation, i.e.
all values of KEL were NA
When setting all concentrations to NA (changed in the data), an error occurs for tc010, 011, 012, 013 and 015 when otherwise unchanged (unchanged mct). No errors occur for tc014 and tc016 when otherwise unchanged (unchanged mct).
All test cases except tc013 provide the following error when setting all concentrations to NA and a partial AUC is asked for by the mct (besides partial AUCs starting from zero). Note that when unchanged, tc010, tc011 and tc015 all contain such partial AUCs.
Error in value[[3L]](cond) : Error in tmp[[2]]: subscript out of bounds
For SDEID 2925168
7. stop(paste0(e, "For SDEID ", unique(data_data[, map_data$SDEID])[i])) at run_M1_SD_computation.R#1701
6. value[[3L]](cond)
5. tryCatchOne(expr, names, parentenv, handlers[[1L]])
4. tryCatchList(expr, classes, parentenv, handlers)
3. tryCatch({
tmp_df <- data_data[data_data[, map_data$SDEID] == unique(data_data[,
map_data$SDEID])[i], ]
tmp_df <- tmp_df[order(tmp_df[, map_data$TIME]), ] ... at run_M1_SD_computation.R#820
2. run_M1_SD_computation(data = merged_data, map = map_data, method = method,
model_regex = model_regex, parameter_list = parameter_list,
return_list = return_list) at run_computation.R#2217
1. run_computation(data = d, map = mct, flag = flags, parameterset = parameterset)
When left unchanged, tc012 gives the following error when all concentrations are set to NA:
Error in value[[3L]](cond) :
Error in if (time[e] == t_last) {: missing value where TRUE/FALSE needed
For SDEID 24280050
7. stop(paste0(e, "For SDEID ", unique(data_data[, map_data$SDEID])[i])) at run_M1_SS_computation.R#2608
6. value[[3L]](cond)
5. tryCatchOne(expr, names, parentenv, handlers[[1L]])
4. tryCatchList(expr, classes, parentenv, handlers)
3. tryCatch({
if (comp_required[["DOSECi"]] || comp_required[["DOSEC"]]) {
dose_ci <- list()
} ... at run_M1_SS_computation.R#1020
2. run_M1_SS_computation(data = merged_data, map = map_data, method = method,
model_regex = model_regex, parameter_list = parameter_list,
return_list = return_list) at run_computation.R#2223
1. run_computation(data = d, map = mct, flag = flags, parameterset = parameterset)
Unlike the other test cases, tc013 provides the following error when all concentrations to NA and does not appear to change when adding a partial AUC.
Error in value[[3L]](cond) :
Error in est_c_0$est_c0: $ operator is invalid for atomic vectors
For SDEID 10589286
7. stop(paste0(e, "For SDEID ", unique(data_data[, map_data$SDEID])[i])) at run_M2_SD_computation.R#1663
6. value[[3L]](cond)
5. tryCatchOne(expr, names, parentenv, handlers[[1L]])
4. tryCatchList(expr, classes, parentenv, handlers)
3. tryCatch({
tmp_df <- data_data[data_data[, map_data$SDEID] == unique(data_data[,
map_data$SDEID])[i], ]
tmp_df <- tmp_df[order(tmp_df[, map_data$TIME]), ] ... at run_M2_SD_computation.R#805
2. run_M2_SD_computation(data = merged_data, map = map_data, method = method,
model_regex = model_regex, parameter_list = parameter_list,
return_list = return_list) at run_computation.R#2229
1. run_computation(data = d, map = mct, flag = flags, parameterset = parameterset)
Comparison of AUC0-24 and AUCLAST appear to have fairly large differences, with an average difference of 13.3 for AUC0-24 and 39.5 for AUCTLAST.
While not a comprehensive check on the qualification of the computation engine and validity of results, this check was done in the course of other testing and it was noticed that these results would not be acceptable relative to the reference results.
> testcase.id <- "tc071"
> p <- "./testcases/tc071_ogormanmt_20180417_005556827PM"
> params["INPUT1"] = file.path(p, "B3461056.in.csv")
> params["parameterset"] ="PARAMETERLIST"
> params["map"] = file.path(p, "tc071_mct_updated.csv")
> params["flags"] = file.path(p, "B3461056_flag_data.in.csv")
> params["OUTPUT"] ="."
> print(params)
INPUT1 parameterset
"./testcases/tc071_ogormanmt_20180417_005556827PM/B3461056.in.csv" "PARAMETERLIST"
map flags
"./testcases/tc071_ogormanmt_20180417_005556827PM/tc071_mct_updated.csv" "./testcases/tc071_ogormanmt_20180417_005556827PM/B3461056_flag_data.in.csv"
OUTPUT REFERENCERESULTS
"." "./testcases/tc071_ogormanmt_20180417_005556827PM/B3461056_pp_calc.xml.csv"
>
> mct <- read_csv(params["map"])
Parsed with column specification:
cols(
.default = col_character(),
AUCNPAIR = col_integer(),
FLGNOCMAX = col_integer(),
FLGACCEPTPREDOSECRIT = col_double(),
SPANRATIOCRIT = col_integer(),
DP.DEFAULT = col_integer(),
SF.DEFAULT = col_integer(),
OPTIMIZEKEL = col_integer(),
INCLUDEINTERPOLATION = col_integer(),
INCLUDEEXTRAPOLATION = col_integer(),
AUC.1.T1 = col_integer(),
AUC.1.T2 = col_integer(),
AUC.2.T1 = col_integer(),
AUC.2.T2 = col_integer(),
AUC.3.T1 = col_integer(),
AUC.3.T2 = col_integer()
)
See spec(...) for full column specifications.
> write_file(toJSON(mct, na="string"), path=file.path(p, "tc071_mct_updated.json"))
> params["map"] = file.path(p, "tc071_mct_updated.json")
>
> flags <- read_csv(params["flags"])
Parsed with column specification:
cols(
PKDATAROWID = col_integer(),
FLGEXSDE = col_integer(),
FLGEXKEL = col_integer(),
FLGEXAUC = col_integer(),
FLGEXST = col_integer(),
NDOSEI = col_integer(),
PKPCOM = col_character(),
MIDPNTN = col_character(),
MIDPNTA = col_character(),
MIDPNTNU = col_character(),
MIDPNTAU = col_character(),
RATEN = col_character(),
RATEA = col_character(),
RATENU = col_character(),
RATEAU = col_character()
)
> write_file(toJSON(flags, na="string"), path=file.path(p, "tc071_flags.json"))
> params["flags"] = file.path(p, "tc071_flags.json")
>
> params["REFERENCERESULTS"] = file.path(p, "B3461056_pp_calc.xml.csv")
> conc <- read.csv(file=params["INPUT1"], header=TRUE, sep=",", as.is=TRUE)
> vlist <- c("PKDATAROWID", "PKPTMS", "PKCNCN", "PERIOD", "TREATXT")
>
> # Read mct
> mct <- params["map"]
> ###mct <- read_file(mct)
> ###mct <- gsub("[\\]+", "", mct)
> mct <- fromJSON(mct)
>
> # Read flags
> flags <- params["flags"]
> ###flags <- read_file(flags)
> ###flags <- gsub("[\\]+", "", flags)
> flags <- fromJSON(flags)
>
> # ParameterSet
> if(is.na(params["parameterset"])) { params["parameterset"] <- "PARAMETERDISPLAYLIST" }
> parameterset <- params["parameterset"]
>
> # FLAGS is a subset for the specific profile, subset concentration dataframe
> # to those records by PKDATAROWID
> d <- conc[is.element(conc$PKDATAROWID, flags$PKDATAROWID),]
>
> # Add in missing required parameters for virtual compute call
> klist <- c("KEL", "KELRSQ", "KELNOPT", "THALFF")
> if(!is.element("PARAMETERDISPLAYLIST", names(mct))) { mct$PARAMETERDISPLAYLIST <- "" }
> for(i in klist) {
+ ###if(regexpr(i, mct$PARAMETERDISPLAYLIST, ignore.case=TRUE)==-1) { mct$PARAMETERDISPLAYLIST <- c(mct$PARAMETERDISPLAYLIST, i) }
+ if(regexpr(i, mct$PARAMETERDISPLAYLIST, ignore.case=TRUE)==-1) { mct$PARAMETERDISPLAYLIST <- paste(mct$PARAMETERDISPLAYLIST, i, sep=";") }
+ }
>
> # Remove any leading semi-colons ";"
> mct$PARAMETERDISPLAYLIST <- gsub("^;*", "", mct$PARAMETERDISPLAYLIST, ignore.case=TRUE, perl=TRUE)
>
> # For virtual compute call only: Subset the parameters to be calculated to those in parameterdisplaylist
> if(!is.element("PARAMETERLIST", names(mct))) { mct$PARAMETERLIST <- "" }
>
> # Check whether TIME selection has been set in MCT - default to NOMINAL
> if(!is.element("TIME", names(mct))) { mct$TIME <- "NOMINAL" }
>
> # Determine whether there are any points selected for inclusion in the terminal elimination phase via
> # FLGEXKEL (0=not excluded/included; 1=excluded/not included)
> # ***What happens if there are no flags provided?***
> n <- factor(flags$FLGEXKEL, levels=c(0,1))
> ninc <- length(n[n==0])
> nexc <- length(n[n==1])
> model_spec(mct)
[1] "openNCA: Model: M1 DosingType: SS AUCMETHOD: LINLOG"
> mct$TIME #<- "Nominal"
[1] "NOMINAL"
> mct$NOMTIME
[1] "PKPTMS"
> mct$NOMTIMEU
[1] "PKPTMU"
> mct$ACTTIME #<- "PKATPD"
[1] "PKATPD"
> mct$ACTTIMEU
[1] "PKATPDU"
>
> mct$PARAMETERLIST
[1] "AUCALL;AUCALLDN;AUCINFO;AUCINFOC;AUCINFODN;AUCINFP;AUCINFPC;AUCINFPDN;AUCLAST;AUCLASTi;AUCLASTDNi;AUCLASTCi;AUCT;AUCT1_T2;AUCTAUi;AUCTAUDNi;AUCXPCTO;AUCXPCTP;AUMCTAUi;CAVi;CLASTi;CLFO;CLFTAUi;CLFTAUWi;CLO;CLRTAUi;CMAXi;CMAXCi;CMAXDNi;CMINi;DOSECi;CTROUGHi;CTROUGHENDi;DIi;DOSEi;KEL;KELNOPT;KELRSQ;KELRSQA;KELTMHI;KELTMLO;LASTTIME;MRTEVIFOi;MRTEVIFPi;PTFi;PTRi;PTROUGHRi;PTROUGHREND;TAU;TAUi;THALF;THALFF;TLAG;TLAST;TMAXi;TMINi;TOLD;VZFP;VZFTAUi;VZFTAUWi"
> mct$PARAMETERDISPLAYLIST
[1] "SUBJID;TREATXT;PKBDFLD;PKTERM;PERIODU;PERIOD;VISITU;VISIT;DOSE;DOSEU;ACTTRT;AUCTAU;AUCLASTi;AUCXPCTP;CLFTAUi;CMAX;CMAXi;KEL;KELNOPT;KELRSQ;KELTMHI;KELTMLO;THALF;TLAST;TMAX;TMAXi;VZFP;CAV;CTROUGH;CMIN;PTR;PTF;RACC;RSS;RACCMAXi;THALFF"
>
> parameterset <- "PARAMETERDISPLAYLIST"
> parameterset <- "PARAMETERLIST"
> parameterset
[1] "PARAMETERLIST"
>
> # Parmeters for M1SS
> model_parameters("M1|M1SS")
[1] "AUCALL" "AUCALLDN" "AUCDN" "AUCINFO" "AUCINFOC" "AUCINFODN" "AUCINFOi" "AUCINFP" "AUCINFPC" "AUCINFPDN" "AUCINFPi"
[12] "AUCLAST" "AUCLASTi" "AUCLASTC" "AUCLASTCi" "AUCLASTDN" "AUCLASTDNi" "AUCT" "AUCT1_T2" "AUCTAUDNi" "AUCTAU" "AUCTAUi"
[23] "AUCTDN" "AUCXPCTO" "AUCXPCTOi" "AUCXPCTP" "AUCXPCTPi" "AUMCINFO" "AUMCINFOi" "AUMCINFP" "AUMCINFPi" "AUMCLAST" "AUMCLASTi"
[34] "AUMCTAUi" "AUMCXPTO" "AUMCXPTOi" "AUMCXPTP" "AUMCXPTPi" "CAVi" "CEST" "CLAST" "CLASTi" "CLFO" "CLFOW"
[45] "CLFP" "CLFPW" "CLFTAUi" "CLFTAUWi" "CMAX" "CMAXi" "CMAXDN" "CMAXDNi" "CMIN" "CMINi" "CMINDN"
[56] "CMINDNi" "CTROUGHi" "CTROUGHENDi" "DIi" "DOSE" "DOSEi" "DOSEC" "DOSECi" "F" "FREL" "FRELLAST"
[67] "FRELLASTi" "FTAUi" "KEL" "KELC0" "KELNOPT" "KELR" "KELRSQ" "KELRSQA" "KELTMHI" "KELTMLO" "LASTTIME"
[78] "LASTTIMEi" "MRTEVIFO" "MRTEVIFOi" "MRTEVIFP" "MRTEVIFPi" "MRTLAST" "MRTLASTi" "PTF" "PTFi" "PTR" "PTRi"
[89] "PTROUGHRi" "PTROUGHRENDi" "RACi" "RACCMAXi" "RACCMINi" "RSSi" "TAU" "TAUi" "THALF" "THALFF" "TLAG"
[100] "TLAST" "TLASTi" "TMAX" "TMAXi" "TMIN" "TMINi" "TOLD" "TOLDi" "VZFO" "VZFOW" "VZFP"
[111] "VZFPW" "VZFTAUi" "VZFTAUWi"
>
> k <- parameter_required("OUTPUTUNIT$", names(mct), simplify=FALSE)
> k <- names(k)
> mct[,k]
TIMEOUTPUTUNIT AMOUNTOUTPUTUNIT DOSEOUTPUTUNIT VOLUMEOUTPUTUNIT CONCOUTPUTUNIT KELOUTPUTUNIT CLOUTPUTUNIT AUCOUTPUTUNIT AUMCOUTPUTUNIT AUCNORMOUTPUTUNIT AUROUTPUTUNIT
1 HR NA MG L NG/ML 1/HR L/HR NG.HR/ML NG.HR.HR/ML NG.HR/ML/MG NA
CONCNORMOUTPUTUNIT RATEOUTPUTUNIT VOLUMENORMOUTPUTUNIT CLNORMOUTPUTUNIT
1 NG/ML/MG NA L/KG ML/HR/KG
>
> k <- parameter_required("^AUC.", names(mct), simplify=FALSE)
> k <- names(k)
> mct[,k]
AUCMETHOD AUCOUTPUTUNIT AUCNORMOUTPUTUNIT AUCNPAIR AUC.1.T1 AUC.1.T2 AUC.2.T1 AUC.2.T2 AUC.3.T1 AUC.3.T2
1 LINLOG NG.HR/ML NG.HR/ML/MG 0 0 12 0 26 0 24
>
> mct.saved <- mct
>
> j <- parameter_required("DOSE", names(mct), simplify=FALSE)
> mct[, names(j)]
DOSE1 DOSE1U DOSEOUTPUTUNIT DOSETIM DOSETIMESTOP FLGACCEPTPREDOSECRIT
1 DOSE DOSEUNI MG DOSETIME DOSETIMESTOP 0.05
>
> j <- parameter_required("TAU|TOLD", names(mct), simplify=FALSE)
> mct[, names(j)]
TOLD1 TOLD1U TAU1 TAU1U FLGACCEPTTAU
1 TOLD TOLDU TAU TAUU FLGACCEPTTAU
>
> #mct$OPTIMIZEKEL <- 0 # don't optimize
> #mct$OPTIMIZEKEL <- 1 # optimize
> mct$OPTIMIZEKEL
[1] 0
> mct$INCLUDEEXTRAPOLATION
[1] 1
> mct$INCLUDEINTERPOLATION
[1] 1
> #mct$FLGNOCMAX <- 0 # permit CMAX in optimal selections for KEL
> #mct$FLGNOCMAX <- 1 # do NO permit CMAX in optimal selections for KEL
> mct$FLGNOCMAX
[1] 1
>
> table(d$PKPTMS, d$TREATXT)
61 mgA Tafa free acid cap 80 mg Tafa meglumine cap
0 30 30
0.5 30 30
1 30 30
2 30 30
3 30 30
4 30 30
6 30 30
8 30 30
12 30 30
24 30 30
>
> j <- parameter_required("^AUC.[0-9]+?.", names(mct), simplify=FALSE)
> mct[, names(j)]
AUC.1.T1 AUC.1.T2 AUC.2.T1 AUC.2.T2 AUC.3.T1 AUC.3.T2
1 0 12 0 26 0 24
>
> (tclabel <- paste0("results.", testcase.id, ".", mct$MODEL, ".", mct$DOSINGTYPE, ".", mct$AUCMETHOD))
[1] "results.tc071.M1.SS.LINLOG"
> results_list <- run_computation(data=d, map=mct, flag=flags, parameterset=parameterset)
unit_conversion : Unit Class 1 (Time) time_col: TMAX1 TMIN1 TLAST TLAG KELTMLO KELTMHI THALF LASTTIME MRTEVIFO1 MRTEVIFP1 TAU1 parameters are scaled from HR to HR via scaling factor: 1
unit_conversion : Unit Class 3 (Dose) dose_col: DOSE1 parameters are scaled from MG to MG via scaling factor: 1
unit_conversion : Unit Class 4 (Volume) volume_col: VZFTAU1 parameters are scaled from ML to L via scaling factor: 0.001
unit_conversion : Unit Class 5 (Amount/Volume) conc_col: CMAX1 CMIN1 CLAST1 CTROUGH1 CTROUGHEND1 CAV1 parameters are scaled from NG/ML to NG/ML via scaling factor: 1
unit_conversion : Unit Class 6 (1/Time) kel_col: KEL parameters are scaled from 1/HR to 1/HR via scaling factor: 1
unit_conversion : Unit Class 7 (Volume/Time) cl_col: CLFTAU1 parameters are scaled from ML/HR to L/HR via scaling factor: 0.001
unit_conversion : Unit Class 8: (Amount.Time/Volume) auc_col: AUCALL AUCLAST AUCLAST1 AUCLASTC1 AUCINFO AUCINFOC AUCINFP AUCINFPC AUCTAU1 parameters are scaled from NG.HR/ML to NG.HR/ML via scaling factor: 1
unit_conversion : Unit Class 9: (Amount.Time.Time/Volume) aumc_col: AUMCTAU1 parameters are scaled from NG.HR.HR/ML to NG.HR.HR/ML via scaling factor: 1
unit_conversion : Unit Class 12: ([Amount/Volume]/Amount) concdn_col: CMAXDN1 parameters are scaled from NG/ML/MG to NG/ML/MG via scaling factor: 1
Warning messages:
1: In run_M1_SS_computation(data = merged_data, map = map_data, method = method, :
Flag 'FLGEMESIS' is not present in the dataset
2: In unit_conversion(data = data_data, map = map_data, result = computation_df, :
'AMOUNTU' #2# is not present in the dataset provided via 'map'
3: In unit_conversion(data = data_data, map = map_data, result = computation_df, :
'CONCU' and 'DOSEU' values provided via 'map' is not present in the dataset provided via 'data'
4: In unit_conversion(data = data_data, map = map_data, result = computation_df, :
'PKPTMU' and 'CONCU' #10# are not present in the dataset provided via 'map'
5: In unit_conversion(data = data_data, map = map_data, result = computation_df, :
'CONCU' and 'NORMBSU' value provided via 'map' #13# are not present in the dataset provided via 'data'
6: In unit_conversion(data = data_data, map = map_data, result = computation_df, :
'CONCU', 'PKPTMU and 'NORMBSU' #19# are not present in the dataset provided via 'map'
> r <- results_list$data_out
> e <- results_list$est_data
> f <- results_list$flag_data
> # review eNCA Results
> results_list$ref <- read.csv(file=params["REFERENCERESULTS"], header=TRUE, sep=",", as.is=TRUE)
>
> k <- match(r$SDEID, results_list$ref$SDEID)
> x <- r$AUC0_24
> y <- results_list$ref$AUC0..24[k]
> compare(y, x, tolerance=10^-16)
36/60 mismatches (average diff: 13.3)
[1] 150481 - 150481 == -9.90e-10
[2] 132118 - 132138 == -1.97e+01
[3] 136703 - 136756 == -5.28e+01
[5] 176346 - 176346 == -1.02e-09
[6] 176465 - 176465 == -9.90e-10
[8] 158495 - 158495 == -9.90e-10
[10] 158790 - 158783 == 7.65e+00
[11] 137383 - 137373 == 1.05e+01
[13] 249063 - 249063 == -9.90e-10
...
> x <- r$AUCLAST
> y <- results_list$ref$AUCLAST[k]
> compare(y, x, tolerance=10^-16)
36/60 mismatches (average diff: 39.5)
[1] 150481 - 150481 == -9.90e-10
[2] 132118 - 132138 == -1.97e+01
[3] 136703 - 136756 == -5.28e+01
[5] 176346 - 176346 == -1.02e-09
[6] 176465 - 176465 == -9.90e-10
[8] 158495 - 158495 == -9.90e-10
[10] 158790 - 158783 == 7.65e+00
[11] 137383 - 137373 == 1.05e+01
[13] 249063 - 249063 == -9.90e-10
...
...
tc012 M1SS single tau testcase:
> j <- parameter_required("^AUC[0-9]+?_[0-9]+?", names(r), simplify=FALSE)
> r[, c("SDEID", names(j))]
SDEID AUC0_12 AUC0_8 AUC13_24 AUC0_10 AUC12_30
1 130509330 106.28661 68.54661 101.01331 90.44661 112.81663
2 147299665 127.75800 74.57500 23.49161 101.57500 73.56096
3 24280050 121.70093 73.21000 22.86411 97.93000 69.57968
4 246409536 95.85319 60.81320 98.71463 80.49320 116.48335
5 246953363 132.22544 70.38500 35.31717 101.62500 101.83405
6 249265153 105.81029 72.25441 91.54349 91.66109 105.24380
7 283369224 114.04271 71.71271 99.91402 95.98271 115.81051
8 292748221 122.16927 79.47927 112.93158 103.56927 130.96473
9 297583299 132.20000 76.39500 26.74885 105.83036 78.93559
10 328228072 88.32373 59.80799 77.48153 76.38155 89.02996
11 330747542 112.24156 56.16500 29.08473 84.02500 88.21482
12 331625416 122.09137 71.06137 126.62559 99.92137 148.21804
13 331909809 138.43017 79.18000 30.52975 108.94000 91.42768
14 361955746 122.78796 72.57998 21.54994 99.22797 67.70626
15 372645306 145.30759 86.97000 34.52197 117.55135 96.67961
16 392032034 120.81805 70.88500 33.59945 96.32397 91.33066
17 45831434 135.53617 84.53981 30.15169 110.98868 85.08787
18 494439910 112.34286 76.28286 105.79389 97.01286 122.01738
19 543244472 122.89782 78.21380 108.80205 102.91218 120.43644
20 545913303 131.41914 72.60000 33.73834 101.18000 100.20367
21 579885120 118.70500 58.86500 35.66342 86.36500 106.65215
22 695874499 104.99736 65.78736 95.46428 87.80736 106.38086
23 702415421 151.49158 95.08500 34.66523 124.71463 92.83962
24 705396261 151.38648 83.33500 46.54758 117.72680 127.06918
25 70745987 109.22692 67.51494 101.45221 91.01704 109.01992
26 708723045 162.47091 86.09000 44.12660 125.28559 126.86082
27 716555376 137.28371 79.19000 35.46160 108.74820 98.07116
28 72354714 165.66456 92.14500 56.67040 128.68500 148.45348
29 777074699 148.96822 86.15000 31.92311 117.23000 95.83193
30 783385801 121.90605 79.12605 109.52263 104.47605 129.27205
31 797620983 129.24303 75.30303 129.72168 106.08303 140.93127
32 856727921 142.12160 84.61160 139.59919 117.28160 158.58041
33 895085608 128.76625 79.23625 126.03772 107.67625 144.30093
34 897885073 132.62947 81.23947 131.13516 109.79947 147.78610
35 916516839 121.50282 76.98282 113.49268 102.06282 129.42216
36 982849649 125.99427 78.86427 110.91988 105.68427 129.27825
where AUC13_24 and AUC12_30 are included to test interpolation/extrapolation. However the estimated concentration dataset doesn't include any values for interpolation or extrapolation.
PKDATAROWID SDEID TIME CEST_KEL CEST_INT CEST_EXT CEST_C0 CEST_TLAST
1 26908252 24280050 12.000 5.467556 NA NA NA NA
2 26908253 24280050 18.000 2.696850 NA NA NA NA
3 26908254 24280050 24.000 1.330210 NA NA NA 1.330210
4 26908341 45831434 8.000 7.594357 NA NA NA NA
5 26908342 45831434 10.000 6.382756 NA NA NA NA
6 26908343 45831434 12.000 5.364453 NA NA NA NA
7 26908344 45831434 18.000 3.184764 NA NA NA NA
8 26908345 45831434 24.000 1.890727 NA NA NA 1.890727
9 26908924 70745987 2.000 5.638244 NA NA NA NA
10 26908925 70745987 4.000 5.382876 NA NA NA NA
11 26908926 70745987 6.000 5.139075 NA NA NA NA
12 26908927 70745987 9.000 4.793919 NA NA NA NA
13 26908928 70745987 12.000 4.471946 NA NA NA NA
14 26908929 70745987 12.333 4.437567 NA NA NA NA
15 26908930 70745987 12.667 4.403351 NA NA NA NA
16 26908931 70745987 13.000 4.369500 NA NA NA NA
17 26908932 70745987 13.500 4.319161 NA NA NA NA
18 26908933 70745987 14.000 4.269402 NA NA NA NA
19 26908934 70745987 16.000 4.076032 NA NA NA NA
20 26908935 70745987 18.000 3.891420 NA NA NA NA
21 26908936 70745987 21.000 3.630061 NA NA NA NA
22 26908937 70745987 24.000 3.386256 NA NA NA 3.386256
23 26908291 72354714 12.000 9.540110 NA NA NA NA
24 26908292 72354714 18.000 5.836801 NA NA NA NA
25 26908293 72354714 24.000 3.571053 NA NA NA 3.571053
26 26908951 130509330 13.500 8.226134 NA NA NA NA
27 26908952 130509330 14.000 7.703235 NA NA NA NA
28 26908953 130509330 16.000 5.923561 NA NA NA NA
29 26908954 130509330 18.000 4.555043 NA NA NA NA
30 26908955 130509330 21.000 3.071546 NA NA NA NA
31 26908956 130509330 24.000 2.071197 NA NA NA 2.071197
32 26908018 147299665 12.000 5.948676 NA NA NA NA
33 26908019 147299665 18.000 2.791242 NA NA NA NA
34 26908020 147299665 24.000 1.309709 NA NA NA 1.309709
35 26908532 246409536 13.000 7.993709 NA NA NA NA
36 26908533 246409536 13.500 7.556942 NA NA NA NA
37 26908534 246409536 14.000 7.144040 NA NA NA NA
38 26908535 246409536 16.000 5.706041 NA NA NA NA
39 26908536 246409536 18.000 4.557492 NA NA NA NA
40 26908537 246409536 21.000 3.253213 NA NA NA NA
41 26908538 246409536 24.000 2.322197 NA NA NA 2.322197
42 26908317 246953363 12.000 7.453740 NA NA NA NA
43 26908318 246953363 18.000 3.934901 NA NA NA NA
44 26908319 246953363 24.000 2.077272 NA NA NA 2.077272
45 26908866 249265153 1.500 7.301496 NA NA NA NA
46 26908867 249265153 2.000 7.158180 NA NA NA NA
47 26908868 249265153 4.000 6.612501 NA NA NA NA
48 26908869 249265153 6.000 6.108420 NA NA NA NA
49 26908870 249265153 9.000 5.423425 NA NA NA NA
50 26908871 249265153 12.000 4.815245 NA NA NA NA
51 26908872 249265153 12.333 4.752090 NA NA NA NA
52 26908873 249265153 12.667 4.689577 NA NA NA NA
53 26908874 249265153 13.000 4.628071 NA NA NA NA
54 26908875 249265153 13.500 4.537230 NA NA NA NA
55 26908876 249265153 14.000 4.448172 NA NA NA NA
56 26908877 249265153 16.000 4.109081 NA NA NA NA
57 26908878 249265153 18.000 3.795840 NA NA NA NA
58 26908879 249265153 21.000 3.370176 NA NA NA NA
59 26908880 249265153 24.000 2.992246 NA NA NA 2.992246
60 26908513 283369224 13.000 9.017636 NA NA NA NA
61 26908514 283369224 13.500 8.412782 NA NA NA NA
62 26908515 283369224 14.000 7.848499 NA NA NA NA
63 26908516 283369224 16.000 5.945310 NA NA NA NA
64 26908517 283369224 18.000 4.503626 NA NA NA NA
65 26908518 283369224 21.000 2.969232 NA NA NA NA
66 26908519 283369224 24.000 1.957609 NA NA NA 1.957609
67 26908893 292748221 13.000 9.929105 NA NA NA NA
68 26908894 292748221 13.500 9.299885 NA NA NA NA
69 26908895 292748221 14.000 8.710541 NA NA NA NA
70 26908896 292748221 16.000 6.703709 NA NA NA NA
71 26908897 292748221 18.000 5.159233 NA NA NA NA
72 26908898 292748221 21.000 3.483295 NA NA NA NA
73 26908899 292748221 24.000 2.351772 NA NA NA 2.351772
74 26908108 297583299 10.000 7.328109 NA NA NA NA
75 26908109 297583299 12.000 5.880090 NA NA NA NA
76 26908110 297583299 18.000 3.037806 NA NA NA NA
77 26908485 328228072 1.000 6.393740 NA NA NA NA
78 26908486 328228072 1.500 6.263443 NA NA NA NA
79 26908487 328228072 2.000 6.135801 NA NA NA NA
80 26908488 328228072 4.000 5.650722 NA NA NA NA
81 26908489 328228072 6.000 5.203992 NA NA NA NA
82 26908490 328228072 9.000 4.599235 NA NA NA NA
83 26908491 328228072 12.000 4.064757 NA NA NA NA
84 26908492 328228072 12.333 4.009399 NA NA NA NA
85 26908493 328228072 12.667 3.954633 NA NA NA NA
86 26908494 328228072 13.000 3.900775 NA NA NA NA
87 26908495 328228072 13.500 3.821282 NA NA NA NA
88 26908496 328228072 14.000 3.743409 NA NA NA NA
89 26908497 328228072 16.000 3.447465 NA NA NA NA
90 26908498 328228072 18.000 3.174918 NA NA NA NA
91 26908499 328228072 21.000 2.805961 NA NA NA NA
92 26908500 328228072 24.000 2.479879 NA NA NA 2.479879
93 26908304 330747542 12.000 6.886258 NA NA NA NA
94 26908305 330747542 18.000 3.393008 NA NA NA NA
95 26908306 330747542 24.000 1.671808 NA NA NA 1.671808
96 26908817 331625416 13.000 12.769752 NA NA NA NA
97 26908818 331625416 13.500 10.631781 NA NA NA NA
98 26908819 331625416 14.000 8.851760 NA NA NA NA
99 26908057 331909809 12.000 7.001066 NA NA NA NA
100 26908058 331909809 18.000 3.485191 NA NA NA NA
101 26908059 331909809 24.000 1.734958 NA NA NA 1.734958
102 26908042 361955746 8.000 7.027679 NA NA NA NA
103 26908043 361955746 10.000 6.206793 NA NA NA NA
104 26908044 361955746 12.000 5.481793 NA NA NA NA
105 26908238 372645306 10.000 7.457668 NA NA NA NA
106 26908239 372645306 12.000 6.344770 NA NA NA NA
107 26908240 372645306 18.000 3.907100 NA NA NA NA
108 26908082 392032034 10.000 6.498894 NA NA NA NA
109 26908083 392032034 12.000 5.705948 NA NA NA NA
110 26908084 392032034 18.000 3.861828 NA NA NA NA
111 26908837 494439910 13.500 8.242931 NA NA NA NA
112 26908838 494439910 14.000 7.749338 NA NA NA NA
113 26908839 494439910 16.000 6.053358 NA NA NA NA
114 26908840 494439910 18.000 4.728551 NA NA NA NA
115 26908841 494439910 21.000 3.264568 NA NA NA NA
116 26908842 494439910 24.000 2.253841 NA NA NA 2.253841
117 26908960 543244472 1.000 7.597843 NA NA NA NA
118 26908961 543244472 1.500 7.479809 NA NA NA NA
119 26908962 543244472 2.000 7.363608 NA NA NA NA
120 26908963 543244472 4.000 6.916580 NA NA NA NA
121 26908964 543244472 6.000 6.496690 NA NA NA NA
122 26908965 543244472 9.000 5.914163 NA NA NA NA
123 26908966 543244472 12.000 5.383868 NA NA NA NA
124 26908967 543244472 12.333 5.328019 NA NA NA NA
125 26908968 543244472 12.667 5.272584 NA NA NA NA
Add partial AUCs:
mct$AUC.4.T1 <- 0
mct$AUC.4.T2 <- 30
# Extrapolate Partial AUC
mct$AUC.5.T1 <- 0
mct$AUC.5.T2 <- 124
j <- parameter_required("^AUC.[0-9]+?.", names(mct), simplify=FALSE)
mct[, names(j)]
AUC.1.T1 AUC.1.T2 AUC.2.T1 AUC.2.T2 AUC.3.T1 AUC.3.T2 AUC.4.T1 AUC.4.T2 AUC.5.T1 AUC.5.T2
1 0 7 8 48 0 64 0 30 0 124
> results_list <- run_computation(data=d, map=mct, flag=flags, parameterset=parameterset)
AUC0_7 AUC8_48 AUC0_64 AUC0_30 AUC0_124
1 5.150750 16.115864 23.59851 19.70926 24.36632
2 16.630430 27.186570 47.51955 43.74855 47.72142
3 1826.885933 1140.509935 3155.39210 2946.43465 3186.01791
4 759.772228 1482.006413 2543.66350 1857.10614 2694.60452
5 6447.260530 5524.243972 12915.26200 11697.15968 13171.68935
6 2026.373649 1432.192203 3683.11590 3400.62904 3729.67352
7 16.023791 46.488059 73.48402 50.29528 81.36431
8 19.037500 62.309949 91.99822 69.28342 99.67902
9 395.187556 414.853089 883.42274 772.97428 910.68799
10 3.982875 15.775430 22.50988 17.76147 25.19713
11 4278.933737 2468.641048 7170.07381 6754.99124 7254.46948
12 7730.206336 5323.498495 13984.33804 12936.52938 14211.86415
13 11.910000 51.133415 70.47629 54.10371 76.67034
14 94.077277 145.965750 268.55542 222.07822 290.29162
15 77.181437 108.658798 204.54646 169.21880 211.74998
16 5228.563212 4459.884946 10432.14093 9395.26444 10703.89267
17 22.587594 61.264286 93.30063 72.58782 97.01257
18 471.496268 542.503402 1117.77753 950.86283 1162.13224
19 3620.713623 3105.414236 7252.21114 6526.48819 7477.50030
20 53.298750 131.477784 203.91558 179.66670 208.43803
21 6670.725819 4731.692389 12129.35766 11050.42709 12426.39270
22 1628.484634 1407.499421 3307.33363 2908.00294 3430.97916
23 209.312058 332.789228 604.37115 508.88000 634.62825
24 7445.501574 5909.793566 14477.65743 12515.36085 15269.39305
25 1055.339216 1175.596164 2437.80067 2163.83949 2518.82981
26 4400.426105 3558.197713 8568.68723 7909.54856 8711.33322
27 41.346250 154.878916 221.93726 173.65382 232.86491
28 7451.020059 6656.170696 15160.12434 13792.24073 15525.50229
29 4.869875 9.248423 15.17517 13.87906 15.53051
30 1290.255720 1627.226746 3215.16872 2704.65531 3429.48143
31 4569.409480 3585.059094 8754.50885 7914.15886 9012.59798
32 405.622354 588.225438 1114.30067 906.41187 1265.21106
33 840.800000 2728.955981 4087.42371 2989.31063 4561.64687
34 1252.107866 1828.834087 3373.54061 2840.56572 3502.13778
35 1681.984359 1188.749998 3068.01559 2800.86999 3124.56661
36 55.151957 191.942856 277.91700 210.41895 304.09100
37 6.878162 11.650839 20.89284 16.35036 22.55152
38 391.805441 946.630254 1508.28984 1124.35012 1576.78256
39 2142.867553 1753.192912 4159.07458 3828.34464 4241.54138
40 110.519647 228.236251 377.13650 303.88578 395.17431
41 3371.716409 4370.179733 8514.10003 7349.36142 8812.14012
42 493.469246 499.497906 1072.45419 954.89993 1090.45759
43 269.998844 458.234651 805.44510 669.69489 833.17675
44 1103.458808 1334.859976 2665.03729 2277.13391 2728.93677
45 10.641990 21.250117 36.06401 28.29687 38.44154
46 18.197500 44.909422 70.60103 56.97479 72.72429
47 5402.856051 3347.044867 9289.05440 8742.69395 9388.28350
48 9.329302 14.832649 26.59689 22.20162 26.93439
4 NA 2925168 7 NA 3.59500000 NA NA NA
1 22289754 2925168 24 1.70263761 NA NA NA NA
6 NA 2925168 30 NA 1.43400000 NA NA NA
7 NA 2925168 30 NA 1.43400000 NA NA NA
2 22289755 2925168 48 0.58780724 NA NA NA NA
5 NA 2925168 64 NA 0.32866667 NA NA NA
3 22289756 2925168 72 0.20293065 NA NA NA 0.20293065
51 NA 40876220 7 NA 1.11575000 NA NA NA
11 22289571 40876220 16 0.49544432 NA NA NA NA
21 22289572 40876220 24 0.36517471 NA NA NA NA
71 NA 40876220 30 NA 0.31600000 NA NA NA
8 NA 40876220 30 NA 0.31600000 NA NA NA
31 22289573 40876220 48 0.14622386 NA NA NA NA
61 NA 40876220 64 NA 0.08246667 NA NA NA
41 22289574 40876220 72 0.05855120 NA NA NA 0.05855120
42 NA 63536292 7 NA 1.03375000 NA NA NA
12 22289531 63536292 12 0.51896071 NA NA NA NA
22 22289532 63536292 16 0.36933771 NA NA NA NA
32 22289533 63536292 24 0.18706907 NA NA NA 0.18706907
62 NA 63536292 30 NA 0.14325000 NA NA NA
72 NA 63536292 30 NA 0.14325000 NA NA NA
52 NA 63536292 64 NA 0.00000000 NA NA NA
43 NA 111299859 7 NA 1.31050000 NA NA NA
13 22289597 111299859 16 0.58377725 NA NA NA NA
23 22289598 111299859 24 0.37399383 NA NA NA NA
63 NA 111299859 30 NA 0.31462500 NA NA NA
73 NA 111299859 30 NA 0.31462500 NA NA NA
33 22289599 111299859 48 0.09833684 NA NA NA 0.09833684
53 NA 111299859 64 NA 0.03250000 NA NA NA
54 NA 112146587 7 NA 2.98750000 NA NA NA
14 22289765 112146587 12 1.59483827 NA NA NA NA
24 22289766 112146587 16 1.14287770 NA NA NA NA
34 22289767 112146587 24 0.58690253 NA NA NA NA
74 NA 112146587 30 NA 0.51065000 NA NA NA
81 NA 112146587 30 NA 0.51065000 NA NA NA
44 22289768 112146587 48 0.07948124 NA NA NA 0.07948124
64 NA 112146587 64 NA 0.02586667 NA NA NA
82 NA 123811839 7 NA 164.75000000 NA NA NA
15 22290452 123811839 12 44.59385840 NA NA NA NA
25 22290453 123811839 16 36.10132904 NA NA NA NA
35 22290454 123811839 24 23.66025768 NA NA NA NA
10 NA 123811839 30 NA 19.47000000 NA NA NA
111 NA 123811839 30 NA 19.47000000 NA NA NA
45 22290455 123811839 48 6.66052265 NA NA NA NA
9 NA 123811839 64 NA 2.28000000 NA NA NA
55 22290456 123811839 72 1.87498220 NA NA NA NA
65 22290457 123811839 96 0.52782018 NA NA NA NA
75 22290458 123811839 120 0.14858495 NA NA NA 0.14858495```
When excluding all data point from AUC computations for tc013 the computation engine crashes giving a unique error (not the same error as specified in #35).
flags$FLGEXAUC <- rep(1, nrow(flags)) # exclude all records/data points from KEL calcs
Error in value[[3L]](cond) :
Error in auc_XbpctO(conc = tmp_df[, map_data$CONC], time = tmp_df[, map_data$TIME], : object 'auc_xbpcto' not found
For SDEID 10589286
7. stop(paste0(e, "For SDEID ", unique(data_data[, map_data$SDEID])[i])) at run_M2_SD_computation.R#1708
6. value[[3L]](cond)
5. tryCatchOne(expr, names, parentenv, handlers[[1L]])
4. tryCatchList(expr, classes, parentenv, handlers)
3. tryCatch({
tmp_df <- data_data[data_data[, map_data$SDEID] == unique(data_data[,
map_data$SDEID])[i], ]
tmp_df <- tmp_df[order(tmp_df[, map_data$TIME]), ] ... at run_M2_SD_computation.R#816
2. run_M2_SD_computation(data = merged_data, map = map_data, method = method,
model_regex = model_regex, parameter_list = parameter_list,
return_list = return_list) at run_computation.R#2229
1. run_computation(data = d, map = mct, flag = flags, parameterset = parameterset)
When a partial AUC is added to tc013 it will crash with the error shown in #35.
tc010
# Interpolate Partial AUC
mct$AUC.4.T1 <- 16
mct$AUC.4.T2 <- 30
# Extrapolate Partial AUC
mct$AUC.5.T1 <- 0
mct$AUC.5.T2 <- 10
> j <- parameter_required("^AUC.[0-9]+?.", names(mct), simplify=FALSE)
> mct[, names(j)]
AUC.1.T1 AUC.1.T2 AUC.2.T1 AUC.2.T2 AUC.3.T1 AUC.3.T2 AUC.4.T1 AUC.4.T2 AUC.5.T1 AUC.5.T2
1 0 12 12 24 0 48 16 30 0 10
Resulting parameters include the requested partial AUCs:
> j <- parameter_required("^AUC[0-9]+?_[0-9]+?", names(r), simplify=FALSE)
> r[, c("SDEID", names(j))]
SDEID AUC0_12 AUC12_24 AUC0_48 AUC16_30 AUC0_10
1 130509330 106.28661 112.81663 219.1032 56.19414 90.44661
2 246409536 95.85319 116.48335 212.3365 59.06146 80.49320
3 249265153 105.81029 105.24380 211.0541 48.68464 91.66109
4 283369224 114.04271 115.81051 229.8532 56.68161 95.98271
5 292748221 122.16927 130.96473 253.1340 64.59674 103.56927
6 328228072 88.32373 89.02996 177.3537 42.98849 76.38155
7 331625416 122.09137 148.21804 270.3094 73.89268 99.92137
8 494439910 112.34286 122.01738 234.3602 60.32097 97.01286
9 543244472 122.89782 120.43644 243.3343 65.12749 102.91218
10 695874499 104.99736 106.38086 211.3782 54.37459 87.80736
11 70745987 109.22692 109.01992 218.2468 64.32691 91.01704
12 783385801 121.90605 129.27205 251.1781 63.54793 104.47605
13 797620983 129.24303 140.93127 270.1743 78.46572 106.08303
14 856727921 142.12160 158.58041 300.7020 79.40981 117.28160
15 895085608 128.76625 144.30093 273.0672 74.78059 107.67625
16 897885073 132.62947 147.78610 280.4156 78.28684 109.79947
17 916516839 121.50282 129.42216 250.9250 64.19622 102.06282
18 982849649 125.99427 129.27825 255.2725 64.79428 105.68427
However the estimated concentration dataset doesn't include the values that require interpolation or extrapolation:
PKDATAROWID SDEID TIME CEST_KEL CEST_INT CEST_EXT CEST_C0 CEST_TLAST
1 26908935 70745987 18.0 4.780859 NA NA NA NA
2 26908936 70745987 21.0 3.498742 NA NA NA NA
3 26908937 70745987 24.0 2.560460 NA NA NA 2.560460
4 26908954 130509330 18.0 4.124984 NA NA NA NA
5 26908955 130509330 21.0 3.017955 NA NA NA NA
6 26908956 130509330 24.0 2.208021 NA NA NA 2.208021
7 26908535 246409536 16.0 5.333360 NA NA NA NA
8 26908536 246409536 18.0 4.376376 NA NA NA NA
9 26908537 246409536 21.0 3.253009 NA NA NA NA
10 26908538 246409536 24.0 2.417997 NA NA NA 2.417997
11 26908877 249265153 16.0 4.922666 NA NA NA NA
12 26908878 249265153 18.0 3.780412 NA NA NA NA
13 26908879 249265153 21.0 2.544177 NA NA NA NA
14 26908880 249265153 24.0 1.712204 NA NA NA 1.712204
15 26908516 283369224 16.0 5.622917 NA NA NA NA
16 26908517 283369224 18.0 4.355208 NA NA NA NA
17 26908518 283369224 21.0 2.968792 NA NA NA NA
18 26908519 283369224 24.0 2.023721 NA NA NA 2.023721
19 26908895 292748221 14.0 8.142487 NA NA NA NA
20 26908896 292748221 16.0 6.390056 NA NA NA NA
21 26908897 292748221 18.0 5.014785 NA NA NA NA
22 26908898 292748221 21.0 3.486372 NA NA NA NA
23 26908899 292748221 24.0 2.423791 NA NA NA 2.423791
24 26908495 328228072 13.5 6.605181 NA NA NA NA
25 26908496 328228072 14.0 6.128861 NA NA NA NA
26 26908497 328228072 16.0 4.543182 NA NA NA NA
27 26908498 328228072 18.0 3.367756 NA NA NA NA
28 26908499 328228072 21.0 2.149370 NA NA NA NA
29 26908500 328228072 24.0 1.371771 NA NA NA 1.371771
30 26908818 331625416 13.5 9.835481 NA NA NA NA
31 26908819 331625416 14.0 9.241418 NA NA NA NA
32 26908820 331625416 16.0 7.202953 NA NA NA NA
33 26908821 331625416 18.0 5.614131 NA NA NA NA
34 26908822 331625416 21.0 3.863140 NA NA NA NA
35 26908823 331625416 24.0 2.658266 NA NA NA 2.658266
36 26908839 494439910 16.0 5.601851 NA NA NA NA
37 26908840 494439910 18.0 4.508992 NA NA NA NA
38 26908841 494439910 21.0 3.256126 NA NA NA NA
39 26908842 494439910 24.0 2.351381 NA NA NA 2.351381
40 26908969 543244472 13.0 8.540985 NA NA NA NA
41 26908970 543244472 13.5 8.080628 NA NA NA NA
42 26908971 543244472 14.0 7.645084 NA NA NA NA
43 26908972 543244472 16.0 6.125348 NA NA NA NA
44 26908973 543244472 18.0 4.907714 NA NA NA NA
45 26908974 543244472 21.0 3.519670 NA NA NA NA
46 26908975 543244472 24.0 2.524206 NA NA NA 2.524206
47 26908572 695874499 14.0 7.097609 NA NA NA NA
48 26908573 695874499 16.0 5.476251 NA NA NA NA
49 26908574 695874499 18.0 4.225272 NA NA NA NA
50 26908575 695874499 21.0 2.863595 NA NA NA NA
51 26908576 695874499 24.0 1.940745 NA NA NA 1.940745
52 26908476 783385801 13.5 8.322697 NA NA NA NA
53 26908477 783385801 14.0 7.852061 NA NA NA NA
54 26908478 783385801 16.0 6.221025 NA NA NA NA
55 26908479 783385801 18.0 4.928789 NA NA NA NA
56 26908480 783385801 21.0 3.475823 NA NA NA NA
57 26908481 783385801 24.0 2.451179 NA NA NA 2.451179
58 26908553 797620983 14.0 9.083895 NA NA NA NA
59 26908554 797620983 16.0 7.349303 NA NA NA NA
60 26908555 797620983 18.0 5.945936 NA NA NA NA
61 26908556 797620983 21.0 4.326944 NA NA NA NA
62 26908557 797620983 24.0 3.148780 NA NA NA 3.148780
63 26908916 856727921 18.0 6.093222 NA NA NA NA
64 26908917 856727921 21.0 4.251489 NA NA NA NA
65 26908918 856727921 24.0 2.966437 NA NA NA 2.966437
66 26908858 895085608 16.0 6.810659 NA NA NA NA
67 26908859 895085608 18.0 5.571924 NA NA NA NA
68 26908860 895085608 21.0 4.123151 NA NA NA NA
69 26908861 895085608 24.0 3.051078 NA NA NA 3.051078
70 26908610 897885073 14.0 8.898941 NA NA NA NA
71 26908611 897885073 16.0 7.213563 NA NA NA NA
72 26908612 897885073 18.0 5.847380 NA NA NA NA
73 26908613 897885073 21.0 4.267550 NA NA NA NA
74 26908614 897885073 24.0 3.114554 NA NA NA 3.114554
75 26908593 916516839 18.0 5.022101 NA NA NA NA
76 26908594 916516839 21.0 3.451510 NA NA NA NA
77 26908595 916516839 24.0 2.372099 NA NA NA 2.372099
78 26908629 982849649 14.0 7.872877 NA NA NA NA
79 26908630 982849649 16.0 6.229732 NA NA NA NA
80 26908631 982849649 18.0 4.929528 NA NA NA NA
81 26908632 982849649 21.0 3.469838 NA NA NA NA
82 26908633 982849649 24.0 2.442379 NA NA NA 2.442379
Execution of M2 SD bolus model with commit 92ca12a
It appears that the 343222739 SDEID is associated with records where PKCNCN is ALL missing, i.e. NA. This needs to be addressed in a way that missing PKCNCN values do not impair execution or processing of profiles and generation of parameters for other profiles.
Links to #38
There were 25 warnings (use warnings() to see them)
[1] "openNCA: Model: M2 DosingType: SD AUCMETHOD: LINLOG"
Error in value[[3L]](cond) :
Error: $ operator is invalid for atomic vectors
For SDEID 343222739
Calls: run_computation ... tryCatch -> tryCatchList -> tryCatchOne -> <Anonymous>
In addition: Warning messages:
1: In run_M2_SD_computation(data = merged_data, map = map_data, method = method, :
Flag 'FLGEMESIS' is not present in the dataset
2: In run_M2_SD_computation(data = merged_data, map = map_data, method = method, :
Flag 'FLGACCEPTTAU' is not present in the dataset
Execution halted
When running tc011 unchanged, columns in data_out are misaligned, each value has been pushed one position to the right. An example is shown below:
SDEID DOSE DOSE.1 DOSEC CMAX1 CMAX2 CMAXDN1 CMAXDN2 FLGACCEPTPREDOSE CMIN1 CMIN2 CLAST1 CLAST2 TMAX1
1 130509330 300 300 300 3e+05 8120 9540 27.06667 0.0318 0 1940 2200 2290 2.20
2 246409536 300 300 300 3e+05 8130 12600 27.10000 0.042 0 945 2240 2240 2.42
3 249265153 300 300 300 3e+05 12200 12100 40.66667 0.0403333333333333 0 1620 1720 2030 1.72
4 283369224 300 300 300 3e+05 8060 12900 26.86667 0.043 0 2060 1990 2520 1.99
5 292748221 300 300 300 3e+05 10900 11500 36.33333 0.0383333333333333 0 2260 2490 2720 2.49
TMAX2 TMIN1 TMIN2 TLAST TLAG KEL KELC0 KELTMLO KELTMHI KELNOPT KELRSQ KELRSQA FLGACCEPTKEL THALF
1 1.500 13.000 0 24 24 0 104.16094 26.89550 18 24 3 0.9995933 1 0
2 0.667 12.667 0 12 24 0 98.88024 25.94724 16 24 4 0.9990935 1 0
3 1.000 13.000 0 24 24 0 132.00860 40.69022 16 24 4 0.9992316 1 0
4 2.000 12.667 0 24 24 0 127.73909 43.40940 16 24 4 0.9981222 1 0
5 1.000 12.667 0 24 24 0 121.17629 44.41440 14 24 5 0.9974224 1 0
This issue with tc011 was brought up in a previous touchpoint meeting and documenting it as an issue so it's not forgotten.
This list has been updated for commit e7df513 on 28MAY20, see individual listings for last commit where testing occurred.
When testing with tc012 (M1SS single tau) with mct$FLGACCEPTKELCRIT <- "KELRSQ>=0.9,KELNOPT>=3,AUCXPCTP<=20.0"
and comparing runs with no-optimization, i.e. Analyst selected points for terminal elimination phase, against optimization selection, there is at least one profile where the optimization routine doesn't select a data points that are appropriate, i.e. see SDEID == 249265153 below. The "optimized" selection of points results in KELNOPT =15, KELRSQ==0.1861215, AUCXPCTP==41.6975933 and thus FLGACCEPTKEL==0. The analyst chosen points ensures that KELRSQ is > 0.9, i.e. 0.9992316 but with KELNOPT==4 and AUCXPCTP==10.945719. This occurs with SDEID==328228072 as well below.
It appears that the optimization routine is favoring KELNOPT over KELRSQ and AUCXPCTP but this is not acceptable since all 3 constraints must be satisfied to set FLGACCEPTKEL to 1. Note that none of the runs included FLGNOCMAX included in the mct for optimization runs.
SDEID SUBJID.nonopt KEL.nonopt KELRSQ.nonopt KELNOPT.nonopt AUCXPCTP.nonopt FLGACCEPTKEL.nonopt SUBJID.opt KEL.opt KELRSQ.opt KELNOPT.opt AUCXPCTP.opt FLGACCEPTKEL.opt
1 130509330 10011015 0.10416094 0.9995933 3 16.212771 1 10011015 0.13135136 0.9846997 6 12.5824889 1
2 147299665 10011003 0.12611367 0.9999997 3 9.352225 1 10011003 0.12611367 0.9999997 3 9.3522248 1
3 24280050 10011004 0.11779122 0.9970626 3 10.560748 1 10011004 0.11779122 0.9970626 3 10.5607478 1
4 246409536 10011008 0.09888024 0.9990935 4 18.721028 1 10011008 0.11237648 0.9828765 7 16.2926707 1
5 246953363 10011013 0.10647171 0.9975960 3 14.288932 1 10011013 0.10647171 0.9975960 3 14.2889318 1
6 249265153 10011007 0.13200860 0.9992316 4 10.945719 1 10011007 0.03964688 0.1861215 15 41.6975933 0
7 283369224 10011005 0.12773909 0.9981222 4 12.114940 1 10011005 0.13885987 0.9925170 7 10.9264101 1
8 292748221 10011010 0.12117629 0.9974224 5 13.646925 1 10011010 0.13093646 0.9895611 7 12.4274535 1
9 297583299 10011017 0.11094610 0.9995613 3 11.868315 1 10011017 0.11007273 0.9978652 3 11.8988892 1
10 328228072 10011004 0.14969062 0.9950580 6 9.366272 1 10011004 0.04117867 0.2447580 16 40.4450752 0
11 330747542 10011012 0.11796850 0.9998570 3 12.387824 1 10011012 0.11796850 0.9998570 3 12.3878236 1
12 331625416 10011002 0.12460213 0.9915082 6 13.632952 1 10011002 0.36646295 0.9106462 3 0.4556764 1
13 331909809 10011010 0.11625660 0.9974519 3 11.492677 1 10011010 0.11625660 0.9974519 3 11.4926770 1
14 361955746 10011007 0.12801045 0.9985610 3 8.821899 1 10011007 0.06210606 0.9474643 3 30.5465816 1
15 372645306 10011001 0.09831321 0.9996748 3 15.022547 1 10011001 0.08080593 0.9666803 3 19.7487372 1
16 392032034 10011014 0.09183114 0.9877755 3 17.395528 1 10011014 0.06506141 0.9794191 3 27.4692315 0
17 45831434 10011018 0.10127805 0.9946942 3 13.845428 1 10011018 0.08690274 0.9750847 5 16.4738500 1
18 494439910 10011003 0.10851179 0.9956459 4 15.606367 1 10011003 0.12349713 0.9789955 6 13.4756817 1
19 543244472 10011017 0.11081363 0.9974500 7 15.769798 1 10011017 0.03131429 0.2753718 16 49.2507389 0
20 545913303 10011015 0.11387213 0.9984973 3 12.821012 1 10011015 0.11387213 0.9984973 3 12.8210116 1
21 579885120 10011006 0.11481358 0.9999872 3 13.752910 1 10011006 0.11552453 1.0000000 2 13.6630693 0
22 695874499 10011012 0.12966859 0.9995428 5 12.404652 1 10011012 0.13340668 0.9973957 6 11.9834562 1
23 702415421 10011008 0.08612275 0.9998397 3 17.369464 1 10011008 0.08464560 0.9994439 4 17.6835865 1
24 705396261 10011002 0.09244230 0.9944449 3 18.168612 1 10011002 0.06397088 0.9329329 3 28.9398530 0
25 70745987 10011014 0.10407221 0.9999990 3 18.397842 1 10011014 0.02317490 0.1646085 14 57.2468704 0
26 708723045 10011011 0.10622643 0.9943391 3 14.378572 1 10011011 0.08120510 0.9800843 3 21.4741684 1
27 716555376 10011016 0.09397368 0.9974393 3 16.737645 1 10011016 0.08674130 0.9667493 3 18.3131984 1
28 72354714 10011009 0.08188704 0.9829235 3 21.732110 0 10011009 0.08188704 0.9829235 3 21.7321103 0
29 777074699 10011005 0.11443233 0.9968965 3 11.822826 1 10011005 0.11443233 0.9968965 3 11.8228255 1
30 783385801 10011001 0.11642069 0.9909143 6 14.357602 1 10011001 0.12504291 0.9733875 7 13.1751675 1
31 797620983 10011009 0.10594879 0.9922606 5 18.033114 1 10011009 0.09514846 0.9772005 3 20.4857378 1
32 856727921 10011011 0.11996924 0.9998907 3 14.123245 1 10011011 0.15415068 0.9301476 3 9.1711532 1
33 895085608 10011006 0.10037422 0.9990909 4 18.209381 1 10011006 0.33940336 0.9465666 3 0.6162318 1
34 897885073 10011016 0.10498462 0.9992916 5 17.463926 1 10011016 0.15107762 0.9186121 3 9.0145584 1
35 916516839 10011013 0.12501219 0.9987959 3 13.137136 1 10011013 0.12915288 0.9882755 6 12.5495078 1
36 982849649 10011018 0.11704509 0.9996112 5 14.051541 1 10011018 0.12706540 0.9789002 7 12.7246902 1
The latest commit, 1fc479c, has an error when running computation that I believe is caused by asking for partial AUC. The following error is given when running test case 010 and 015 (unchanged) and test cases 013 (with added partial AUC):
Error in value[[3L]](cond) :
Error in doTryCatch(return(expr), name, parentenv, handler): Dataset provided via 'map' does no contain the required columns for interpolating partial areas TOLD1
While the traceback is similar for each testcase I've included them all below along with a reproducible example for test case 013.
Traceback for tc010 (no changes):
Error in doTryCatch(return(expr), name, parentenv, handler): Dataset provided via 'map' does not contain the required columns for interpolating partial areas TOLD1
For SDEID 2925168
7. stop(paste0(e, "For SDEID ", unique(data_data[, map_data$SDEID])[i])) at
run_M1_SD_computation.R#1708
6. value[[3L]](cond)
5. tryCatchOne(expr, names, parentenv, handlers[[1L]])
4. tryCatchList(expr, classes, parentenv, handlers)
3. tryCatch({
tmp_df <- data_data[data_data[, map_data$SDEID] == unique(data_data[,
map_data$SDEID])[i], ]
tmp_df <- tmp_df[order(tmp_df[, map_data$TIME]), ] ... at
run_M1_SD_computation.R#817
2. run_M1_SD_computation(data = merged_data, map = map_data, method = method,
model_regex = model_regex, parameter_list = parameter_list,
return_list = return_list) at
run_computation.R#2217
1. run_computation(data = d, map = mct, flag = flags, parameterset = parameterset)
Traceback for tc013 (with partial AUC)
Error in doTryCatch(return(expr), name, parentenv, handler): Dataset provided via 'map' does not contain the required columns for interpolating partial areas TOLD1
For SDEID 10589286
7. stop(paste0(e, "For SDEID ", unique(data_data[, map_data$SDEID])[i])) at
run_M2_SD_computation.R#1666
6. value[[3L]](cond)
5. tryCatchOne(expr, names, parentenv, handlers[[1L]])
4. tryCatchList(expr, classes, parentenv, handlers)
3. tryCatch({
tmp_df <- data_data[data_data[, map_data$SDEID] == unique(data_data[,
map_data$SDEID])[i], ]
tmp_df <- tmp_df[order(tmp_df[, map_data$TIME]), ] ... at
run_M2_SD_computation.R#804
2. run_M2_SD_computation(data = merged_data, map = map_data, method = method,
model_regex = model_regex, parameter_list = parameter_list,
return_list = return_list) at run_computation.R#2229
1. run_computation(data = d, map = mct, flag = flags, parameterset = parameterset)
Traceback for t015 (no changes)
Error in doTryCatch(return(expr), name, parentenv, handler): Dataset provided via 'map' does not contain the required columns for interpolating partial areas TOLD1
For SDEID 12659123
7. stop(paste0(e, "For SDEID ", unique(data_data[, map_data$SDEID])[i])) at
run_M3_SD_computation.R#1664
6. value[[3L]](cond)
5. tryCatchOne(expr, names, parentenv, handlers[[1L]])
4. tryCatchList(expr, classes, parentenv, handlers)
3. tryCatch({
tmp_df <- data_data[data_data[, map_data$SDEID] == unique(data_data[,
map_data$SDEID])[i], ]
tmp_df <- tmp_df[order(tmp_df[, map_data$TIME]), ] ... at
run_M3_SD_computation.R#813
2. run_M3_SD_computation(data = merged_data, map = map_data, method = method,
model_regex = model_regex, parameter_list = parameter_list,
return_list = return_list) at
run_computation.R#2241
1. run_computation(data = d, map = mct, flag = flags, parameterset = parameterset)
I have attached code below used to run test case 013 with the added partial AUC from 492 to 516.
#### Load Libraries ------------------------------------------------------ ####
library(readr)
library(jsonlite)
library(testthat)
#### Load specific code-base for OpenNCA --------------------------------- ####
# pkgload::load_all(path = "codebase/openNCA-master/openNCA")
# pkgload::load_all(path = "codebase/openNCA-b1977923fc/openNCA") # 11/8-v1
# pkgload::load_all(path = "codebase/openNCA-051805c887/openNCA") # 11/8-v2
pkgload::load_all(path = "codebase/openNCA-1fc479c2ca/openNCA") # 11/11
#### Clean test environment ---------------------------------------------- ####
# Clean environment
rm(params, conc, vlist, results, results_list, r, e, mct, d, flags, p, klist, n,
ninc, nexc, k, rlist, dlist, nlist, slist, df, xmin, xmax, dk, flag_data,
est_data, outputdir)
profileid <- "SDEID"
args = commandArgs(trailingOnly=TRUE)
params = c()
#### Test Case 013 ------------------------------------------------------- ####
# M2 SD
# p <- "C:/Users/tensfeldt/Box Sync/EQuIP Computation Engine/Rudraya_executed_test_cases/tc013"
testcase.id <- "tc013"
p <- "testcase/tc013_M2SD"
params["INPUT1"] = file.path(p, "B2901001.in.csv")
params["parameterset"] ="PARAMETERLIST"
params["map"] = file.path(p, "tc013_mct_updated.csv")
params["flags"] = file.path(p, "B2901001_flag_data.in.csv")
params["OUTPUT"] ="."
print(params)
mct <- read_csv(params["map"])
write_file(toJSON(mct, na="string"), path=file.path(p, "tc013_mct_updated.json"))
params["map"] = file.path(p, "tc013_mct_updated.json")
flags <- read_csv(params["flags"])
write_file(toJSON(flags, na="string"), path=file.path(p, "tc013_flags.json"))
params["flags"] = file.path(p, "tc013_flags.json")
#### Read full concentration dataset ------------------------------------- ####
# Read conc
conc <- read.csv(file=params["INPUT1"], header=TRUE, sep=",", as.is=TRUE)
vlist <- c("PKDATAROWID", "PKPTMS", "PKCNCN", "PERIOD", "TREATXT")
# Read mct
mct <- params["map"]
###mct <- read_file(mct)
###mct <- gsub("[\\]+", "", mct)
mct <- fromJSON(mct)
# Read flags
flags <- params["flags"]
###flags <- read_file(flags)
###flags <- gsub("[\\]+", "", flags)
flags <- fromJSON(flags)
# ParameterSet
if(is.na(params["parameterset"])) { params["parameterset"] <- "PARAMETERDISPLAYLIST" }
parameterset <- params["parameterset"]
# FLAGS is a subset for the specific profile, subset concentration dataframe
# to those records by PKDATAROWID
d <- conc[is.element(conc$PKDATAROWID, flags$PKDATAROWID),]
# Add in missing required parameters for virtual compute call
klist <- c("KEL", "KELRSQ", "KELNOPT", "THALFF")
if(!is.element("PARAMETERDISPLAYLIST", names(mct))) { mct$PARAMETERDISPLAYLIST <- "" }
for(i in klist) {
###if(regexpr(i, mct$PARAMETERDISPLAYLIST, ignore.case=TRUE)==-1) { mct$PARAMETERDISPLAYLIST <- c(mct$PARAMETERDISPLAYLIST, i) }
if(regexpr(i, mct$PARAMETERDISPLAYLIST, ignore.case=TRUE)==-1) { mct$PARAMETERDISPLAYLIST <- paste(mct$PARAMETERDISPLAYLIST, i, sep=";") }
}
# Remove any leading semi-colons ";"
mct$PARAMETERDISPLAYLIST <- gsub("^;*", "", mct$PARAMETERDISPLAYLIST, ignore.case=TRUE, perl=TRUE)
# For virtual compute call only: Subset the parameters to be calculated to those in parameterdisplaylist
if(!is.element("PARAMETERLIST", names(mct))) { mct$PARAMETERLIST <- "" }
# Check whether TIME selection has been set in MCT - default to NOMINAL
if(!is.element("TIME", names(mct))) { mct$TIME <- "NOMINAL" }
# Determine whether there are any points selected for inclusion in the terminal elimination phase via
# FLGEXKEL (0=not excluded/included; 1=excluded/not included)
# ***What happens if there are no flags provided?***
n <- factor(flags$FLGEXKEL, levels=c(0,1))
ninc <- length(n[n==0])
nexc <- length(n[n==1])
#### Final changes to test ----------------------------------------------- ####
### Add partial AUC
mct$AUC.1.T1 <- 492
mct$AUC.1.T2 <- 516
#### Run computation engine ---------------------------------------------- ####
# Run computation
results_list <- run_computation(data = d, map = mct, flag = flags,
parameterset = parameterset)
mct012 M1 SS (single TAU)
> results_list <- run_computation(data=d, map=mct, flag=flags, parameterset=parameterset)
Error in value[[3L]](cond) :
Error in if (flg_no_cmax) {: missing value where TRUE/FALSE needed
For SDEID 24280050
In addition: Warning messages:
1: In run_M1_SS_computation(data = merged_data, map = map_data, method = method, :
Flag 'FLGEMESIS' is not present in the dataset
2: In run_M1_SS_computation(data = merged_data, map = map_data, method = method, :
Kel optimization cannot be performed because 'TMAX', 'TLAST', 'CMAX', 'CLAST', 'AUCLAST' are not part of the calulcated parameters AND Flag 'FLGACCEPTKELCRIT' and Flag 'FLGXKEL' are not present in the dataset
3: In unit_conversion(data = data_data, map = map_data, result = data_data, :
Show Traceback
Rerun with Debug
Error in value[[3L]](cond) :
Error in if (flg_no_cmax) {: missing value where TRUE/FALSE needed
For SDEID 24280050
Note above that one of the warning messages also has misspelled "calculated" as "calulcated".
Note that if NOCMAX is removed from mct this error doesn't occur.
Reproducible, execution running for > 20 minutes
restarts of environment indicate that this is a code issue and not environmental. #
This code was not entirely complete in master branch.
Parameter results are generated but not entirely confirmed as yet.
errors displayed are:
> results_list <- run_computation(data=d, map=mct, flag=flags, parameterset=parameterset)
specific_gravity_adjustment : units not recognized!i: 1 tmp_dose: 30 d: 1 dose[[d]]: 30
i: 1 tmp_dose: 30 d: 1 dose[[d]]: 30
specific_gravity_adjustment : units not recognized!i: 2 tmp_dose: 200 d: 1 dose[[d]]: 200
i: 2 tmp_dose: 200 d: 1 dose[[d]]: 200
specific_gravity_adjustment : units not recognized!i: 3 tmp_dose: 200 d: 1 dose[[d]]: 200
i: 3 tmp_dose: 200 d: 1 dose[[d]]: 200
specific_gravity_adjustment : units not recognized!i: 4 tmp_dose: 200 d: 1 dose[[d]]: 200
i: 4 tmp_dose: 200 d: 1 dose[[d]]: 200
specific_gravity_adjustment : units not recognized!i: 5 tmp_dose: 200 d: 1 dose[[d]]: 200
i: 5 tmp_dose: 200 d: 1 dose[[d]]: 200
specific_gravity_adjustment : units not recognized!i: 6 tmp_dose: 100 d: 1 dose[[d]]: 100
i: 6 tmp_dose: 100 d: 1 dose[[d]]: 100
specific_gravity_adjustment : units not recognized!i: 7 tmp_dose: 30 d: 1 dose[[d]]: 30
i: 7 tmp_dose: 30 d: 1 dose[[d]]: 30
specific_gravity_adjustment : units not recognized!i: 8 tmp_dose: 200 d: 1 dose[[d]]: 200
i: 8 tmp_dose: 200 d: 1 dose[[d]]: 200
specific_gravity_adjustment : units not recognized!i: 9 tmp_dose: 30 d: 1 dose[[d]]: 30
i: 9 tmp_dose: 30 d: 1 dose[[d]]: 30
specific_gravity_adjustment : units not recognized!i: 10 tmp_dose: 200 d: 1 dose[[d]]: 200
i: 10 tmp_dose: 200 d: 1 dose[[d]]: 200
specific_gravity_adjustment : units not recognized!i: 11 tmp_dose: 100 d: 1 dose[[d]]: 100
i: 11 tmp_dose: 100 d: 1 dose[[d]]: 100
specific_gravity_adjustment : units not recognized!i: 12 tmp_dose: 200 d: 1 dose[[d]]: 200
i: 12 tmp_dose: 200 d: 1 dose[[d]]: 200
specific_gravity_adjustment : units not recognized!i: 13 tmp_dose: 200 d: 1 dose[[d]]: 200
i: 13 tmp_dose: 200 d: 1 dose[[d]]: 200
specific_gravity_adjustment : units not recognized!i: 14 tmp_dose: 30 d: 1 dose[[d]]: 30
i: 14 tmp_dose: 30 d: 1 dose[[d]]: 30
specific_gravity_adjustment : units not recognized!i: 15 tmp_dose: 100 d: 1 dose[[d]]: 100
i: 15 tmp_dose: 100 d: 1 dose[[d]]: 100
specific_gravity_adjustment : units not recognized!i: 16 tmp_dose: 30 d: 1 dose[[d]]: 30
i: 16 tmp_dose: 30 d: 1 dose[[d]]: 30
specific_gravity_adjustment : units not recognized!i: 17 tmp_dose: 200 d: 1 dose[[d]]: 200
i: 17 tmp_dose: 200 d: 1 dose[[d]]: 200
specific_gravity_adjustment : units not recognized!i: 18 tmp_dose: 100 d: 1 dose[[d]]: 100
i: 18 tmp_dose: 100 d: 1 dose[[d]]: 100
specific_gravity_adjustment : units not recognized!i: 19 tmp_dose: 200 d: 1 dose[[d]]: 200
i: 19 tmp_dose: 200 d: 1 dose[[d]]: 200
specific_gravity_adjustment : units not recognized!i: 20 tmp_dose: 200 d: 1 dose[[d]]: 200
i: 20 tmp_dose: 200 d: 1 dose[[d]]: 200
specific_gravity_adjustment : units not recognized!i: 21 tmp_dose: 100 d: 1 dose[[d]]: 100
i: 21 tmp_dose: 100 d: 1 dose[[d]]: 100
specific_gravity_adjustment : units not recognized!i: 22 tmp_dose: 200 d: 1 dose[[d]]: 200
i: 22 tmp_dose: 200 d: 1 dose[[d]]: 200
specific_gravity_adjustment : units not recognized!i: 23 tmp_dose: 100 d: 1 dose[[d]]: 100
i: 23 tmp_dose: 100 d: 1 dose[[d]]: 100
specific_gravity_adjustment : units not recognized!i: 24 tmp_dose: 100 d: 1 dose[[d]]: 100
i: 24 tmp_dose: 100 d: 1 dose[[d]]: 100
specific_gravity_adjustment : units not recognized!i: 25 tmp_dose: 200 d: 1 dose[[d]]: 200
i: 25 tmp_dose: 200 d: 1 dose[[d]]: 200
specific_gravity_adjustment : units not recognized!i: 26 tmp_dose: 100 d: 1 dose[[d]]: 100
i: 26 tmp_dose: 100 d: 1 dose[[d]]: 100
specific_gravity_adjustment : units not recognized!i: 27 tmp_dose: 100 d: 1 dose[[d]]: 100
i: 27 tmp_dose: 100 d: 1 dose[[d]]: 100
specific_gravity_adjustment : units not recognized!i: 28 tmp_dose: 100 d: 1 dose[[d]]: 100
i: 28 tmp_dose: 100 d: 1 dose[[d]]: 100
specific_gravity_adjustment : units not recognized!i: 29 tmp_dose: 200 d: 1 dose[[d]]: 200
i: 29 tmp_dose: 200 d: 1 dose[[d]]: 200
specific_gravity_adjustment : units not recognized!i: 30 tmp_dose: 30 d: 1 dose[[d]]: 30
i: 30 tmp_dose: 30 d: 1 dose[[d]]: 30
specific_gravity_adjustment : units not recognized!i: 31 tmp_dose: 200 d: 1 dose[[d]]: 200
i: 31 tmp_dose: 200 d: 1 dose[[d]]: 200
specific_gravity_adjustment : units not recognized!i: 32 tmp_dose: 30 d: 1 dose[[d]]: 30
i: 32 tmp_dose: 30 d: 1 dose[[d]]: 30
specific_gravity_adjustment : units not recognized!i: 33 tmp_dose: 200 d: 1 dose[[d]]: 200
i: 33 tmp_dose: 200 d: 1 dose[[d]]: 200
specific_gravity_adjustment : units not recognized!i: 34 tmp_dose: 30 d: 1 dose[[d]]: 30
i: 34 tmp_dose: 30 d: 1 dose[[d]]: 30
specific_gravity_adjustment : units not recognized!i: 35 tmp_dose: 100 d: 1 dose[[d]]: 100
i: 35 tmp_dose: 100 d: 1 dose[[d]]: 100
specific_gravity_adjustment : units not recognized!i: 36 tmp_dose: 200 d: 1 dose[[d]]: 200
i: 36 tmp_dose: 200 d: 1 dose[[d]]: 200
specific_gravity_adjustment : units not recognized!i: 37 tmp_dose: 100 d: 1 dose[[d]]: 100
i: 37 tmp_dose: 100 d: 1 dose[[d]]: 100
specific_gravity_adjustment : units not recognized!i: 38 tmp_dose: 100 d: 1 dose[[d]]: 100
i: 38 tmp_dose: 100 d: 1 dose[[d]]: 100
specific_gravity_adjustment : units not recognized!i: 39 tmp_dose: 100 d: 1 dose[[d]]: 100
i: 39 tmp_dose: 100 d: 1 dose[[d]]: 100
specific_gravity_adjustment : units not recognized!i: 40 tmp_dose: 100 d: 1 dose[[d]]: 100
i: 40 tmp_dose: 100 d: 1 dose[[d]]: 100
specific_gravity_adjustment : units not recognized!i: 41 tmp_dose: 200 d: 1 dose[[d]]: 200
i: 41 tmp_dose: 200 d: 1 dose[[d]]: 200
specific_gravity_adjustment : units not recognized!i: 42 tmp_dose: 30 d: 1 dose[[d]]: 30
i: 42 tmp_dose: 30 d: 1 dose[[d]]: 30
specific_gravity_adjustment : units not recognized!i: 43 tmp_dose: 100 d: 1 dose[[d]]: 100
i: 43 tmp_dose: 100 d: 1 dose[[d]]: 100
specific_gravity_adjustment : units not recognized!i: 44 tmp_dose: 100 d: 1 dose[[d]]: 100
i: 44 tmp_dose: 100 d: 1 dose[[d]]: 100
specific_gravity_adjustment : units not recognized!i: 45 tmp_dose: 100 d: 1 dose[[d]]: 100
i: 45 tmp_dose: 100 d: 1 dose[[d]]: 100
unit_conversion : Unit Class 1 (Time) time_col: TLAG KELTMLO KELTMHI THALF MIDPT1 MIDPT2 TAU1 TOLD1 parameters are scaled from HR to HR via scaling factor: 1
unit_conversion : Unit Class 2 (Amount) amount_col: AMT.12.00 AMT.24.00 AE.12.00 AE.24.00 AE parameters are scaled from NG to MG via scaling factor: 1e-06
unit_conversion : Unit Class 3 (Dose) dose_col: DOSE1 parameters are scaled from MG to MG via scaling factor: 1
unit_conversion : Unit Class 4 (Volume) volume_col: VOLSUM parameters are scaled from ML to L via scaling factor: 0.001
unit_conversion : Unit Class 6 (1/Time) kel_col: KEL parameters are scaled from 1/HR to 1/HR via scaling factor: 1
unit_conversion : Unit Class 11: ([Volume.Amount]/Volume) aurc_col: AURCALL AURCLAST AURCINFO AURCINFP parameters are scaled from ML.NG/ML to ML.UG/ML via scaling factor: 0.001
unit_conversion : Unit Class 13: (Amount/Time) rate_col: MAXRATE1 RATELAST1 RATE1 RATE2 parameters are scaled from NG/HR to UG/HR via scaling factor: 0.001
There were 50 or more warnings (use warnings() to see the first 50)
Warning messages:
1: In run_M4_SS_computation(data = merged_data, map = map_data, ... :
Flag 'FLGEMESIS' is not present in the dataset
2: In specific_gravity_adjustment(amt_units = volu, map = map) :
NAs introduced by coercion
3: In specific_gravity_adjustment(amt_units = volu, map = map) :
4: In specific_gravity_adjustment(amt_units = volu, map = map) :
NAs introduced by coercion
5: In specific_gravity_adjustment(amt_units = volu, map = map) :
6: In specific_gravity_adjustment(amt_units = volu, map = map) :
NAs introduced by coercion
7: In specific_gravity_adjustment(amt_units = volu, map = map) :
8: In specific_gravity_adjustment(amt_units = volu, map = map) :
NAs introduced by coercion
9: In specific_gravity_adjustment(amt_units = volu, map = map) :
10: In specific_gravity_adjustment(amt_units = volu, map = map) :
NAs introduced by coercion
11: In specific_gravity_adjustment(amt_units = volu, map = map) :
12: In specific_gravity_adjustment(amt_units = volu, map = map) :
NAs introduced by coercion
13: In specific_gravity_adjustment(amt_units = volu, map = map) :
14: In specific_gravity_adjustment(amt_units = volu, map = map) :
NAs introduced by coercion
15: In specific_gravity_adjustment(amt_units = volu, map = map) :
16: In specific_gravity_adjustment(amt_units = volu, map = map) :
NAs introduced by coercion
17: In specific_gravity_adjustment(amt_units = volu, map = map) :
18: In specific_gravity_adjustment(amt_units = volu, map = map) :
NAs introduced by coercion
19: In specific_gravity_adjustment(amt_units = volu, map = map) :
20: In specific_gravity_adjustment(amt_units = volu, map = map) :
NAs introduced by coercion
21: In specific_gravity_adjustment(amt_units = volu, map = map) :
22: In specific_gravity_adjustment(amt_units = volu, map = map) :
NAs introduced by coercion
23: In specific_gravity_adjustment(amt_units = volu, map = map) :
24: In specific_gravity_adjustment(amt_units = volu, map = map) :
NAs introduced by coercion
25: In specific_gravity_adjustment(amt_units = volu, map = map) :
26: In specific_gravity_adjustment(amt_units = volu, map = map) :
NAs introduced by coercion
27: In specific_gravity_adjustment(amt_units = volu, map = map) :
28: In specific_gravity_adjustment(amt_units = volu, map = map) :
NAs introduced by coercion
29: In specific_gravity_adjustment(amt_units = volu, map = map) :
30: In specific_gravity_adjustment(amt_units = volu, map = map) :
NAs introduced by coercion
31: In specific_gravity_adjustment(amt_units = volu, map = map) :
32: In specific_gravity_adjustment(amt_units = volu, map = map) :
NAs introduced by coercion
33: In specific_gravity_adjustment(amt_units = volu, map = map) :
34: In specific_gravity_adjustment(amt_units = volu, map = map) :
NAs introduced by coercion
35: In specific_gravity_adjustment(amt_units = volu, map = map) :
36: In specific_gravity_adjustment(amt_units = volu, map = map) :
NAs introduced by coercion
37: In specific_gravity_adjustment(amt_units = volu, map = map) :
38: In specific_gravity_adjustment(amt_units = volu, map = map) :
NAs introduced by coercion
39: In specific_gravity_adjustment(amt_units = volu, map = map) :
40: In specific_gravity_adjustment(amt_units = volu, map = map) :
NAs introduced by coercion
41: In specific_gravity_adjustment(amt_units = volu, map = map) :
42: In specific_gravity_adjustment(amt_units = volu, map = map) :
NAs introduced by coercion
43: In specific_gravity_adjustment(amt_units = volu, map = map) :
44: In specific_gravity_adjustment(amt_units = volu, map = map) :
NAs introduced by coercion
45: In specific_gravity_adjustment(amt_units = volu, map = map) :
46: In specific_gravity_adjustment(amt_units = volu, map = map) :
NAs introduced by coercion
47: In specific_gravity_adjustment(amt_units = volu, map = map) :
48: In specific_gravity_adjustment(amt_units = volu, map = map) :
NAs introduced by coercion
49: In specific_gravity_adjustment(amt_units = volu, map = map) :
50: In specific_gravity_adjustment(amt_units = volu, map = map) :
NAs introduced by coercion
the UG and MCG units must be made to be synonyms of each other. This must be updated in unit_conversion.R
Also, failures for unit_conversion should not invalidate execution of the computation engine. Other parameters could be generated CMAX/TMAX for example. Make resilient to these types of errors.
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